The Globular Cluster System of NGC 4365 · 2017. 2. 21. · Chapter 2, Chapter 3, Appendices A and...

$: The Globular Cluster System of NGC 4365 · 2017. 2. 21. · Chapter 2, Chapter 3, Appendices A and B have been published together as: \Wide- eld imaging of NGC 4365’s globular cluster$
The Globular Cluster System of NGC 4365

Christina Blom

Presented in fulfillment of the requirements

of the degree of Doctor of Philosophy

May 2013

Faculty of Information and Communication Technology

Swinburne University

i

Abstract

This thesis presents a study of the globular cluster (GC) system of NGC 4365 and the

GCs associated with the W ′ group of galaxies, of which NGC 4365 is the dominant galaxy.

We use the analysis of these GC systems to investigate the evolutionary history of NGC

4365 as well as the ongoing interactions within the W ′ group.

To analyse the photometric properties of NGC 4365’s GC system we combined three

filter imaging from the SuprimeCam instrument on the 8m Subaru telescope with eight,

two filter, pointings from the Advanced Camera for Surveys on the Hubble Space Tele-

scope. To analyse the kinematic properties of NGC 4365’s GC system we obtained spectra

for over 250 GCs around NGC 4365 from the DEep Imaging Multi-Object Spectrograph

(DEIMOS) on the Keck II telescope. The photometric properties of the group GCs were

analysed with square degree, three filter imaging from the MegaCam instrument on the

Canada-France-Hawaii Telescope.

We confirmed that NGC 4365 hosts three GC subpopulations, the usual blue and red

GC subpopulations plus an additional subpopulation at intermediate colour: the green

subpopulation. Photometric analysis showed that the three subpopulations have distinct

radial and azimuthal distributions, different median sizes and mass distributions drawn

from different populations. Using recession velocities calculated from GC spectra we also

determined that each GC subpopulation rotates about the galaxy in a different direction.

Analysis of the spatial distribution of GCs around NGC 4365 in the wider W ′ group

environment uncovered a stream overdensity of GCs between NGC 4365 and a nearby

small lenticular galaxy NGC 4342, extending South West beyond NGC 4342. This GC

stream is spatially coincident with a stellar stream recently presented in the literature.

We found that the recession velocities of the stream GC are consistent with the recession

velocity measured from NGC 4342’s starlight.

We conclude that NGC 4365 formed two GC subpopulations during two separate in

situ, dissipative star formation episodes (the green GCs forming before the red GCs) and

accreted the blue GCs from smaller galaxies throughout its evolutionary history. We also

conclude that NGC 4365 is currently accreting blue GCs and stars from the small lenticular

galaxy NGC 4342. As predicted by the hierarchical merging models of ΛCDM, we have

observed evidence of ongoing growth of the giant elliptical galaxy NGC 4365 in the nearby

Universe.

ii

iii

Acknowledgements

I begin by acknowledging my parents for their love, guidance, motivation and patience.

I have always known that I am loved by you and that knowledge let me be the exploring

and questioning person that started this thesis. You have guided me through the maze of

getting educated with my curiosity and joy of discovery intact. I am especially grateful

for the constant words of encouragement to dream big, to work hard, to keep my eyes on

the goal and to take one step forward after the other.

Without my Ph.D. supervisor Prof. Duncan A. Forbes, I would not have had access to

the data that contained the discoveries presented in this thesis. More importantly I would

not have had the skills or insight to see what was to be uncovered. Thank you for your

patient teaching, daily availability for questions and discussions, openness to explore new

ideas and consistent reminders of short-term goals. To my co-supervisors; thank you Dr

Lee R. Spitler for the recurrent question ‘What does it mean?’ and thank you Prof. Alister

Graham for encouraging me to pay attention to every detail. I am also indebted to my

collaborators Prof. Jean P. Brodie, Dr Caroline Foster, Dr Aaron J. Romanowsky and Dr

Jay Strader for their support and critical assessment of my work. I would also like to thank

the staff and students at the Centre for Astrophysics and Supercomputing, Swinburne

University for the friendship, community and advice over the years. In particular I would

like to thank Dr. Lina Levin, Vincenzo Pota, Christopher Usher, Sreeja Kartha and Nicola

Pastorello for your individual contributions to life in our corner of the office and my Ph.D.

journey.

Swinburne University has generously supported me through my Ph.D. with scholar-

ships as well as an administrative backbone aiming to give students the best resources for

their degree.

Thank you, my beloved Michael D. Smith, my husband and best friend. You have

been steady, patient and unfailingly kind and generous. I am privileged to be adventuring

through this world with you and I need to tell you that I would not have made it through

this challenge without you. My family and friends scattered throughout the globe have

been for me ‘God’s love with skin on’ during my Ph.D. I would be lost without His Light

and I feel honoured to have had the opportunity to discover parts of His Universe.

iv

v

Declaration

The work presented in this thesis has been carried out in the Centre for Astrophysics &

Supercomputing at Swinburne University of Technology between 2009 and 2013. This

thesis contains no material that has been accepted for the award of any other degree

or diploma. To the best of my knowledge, this thesis contains no material previously

published or written by another author, except where due reference is made in the text

of the thesis. The content of the chapters listed below has appeared in refereed journals.

Minor alterations have been made to the published papers in order to maintain argument

continuity and consistency of spelling and style.

• Chapter 2, Chapter 3, Appendices A and B have been published together as:

“Wide-field imaging of NGC 4365’s globular cluster system: the third subpopulation

revisisted”

Blom, C., Spitler, L. R. and Forbes, D. A., 2012, MNRAS, 420, 37

• Chapter 4 and Appendix C have been published together as:

“The SLUGGS survey: globular cluster system kinematics and substructure in

NGC 4365”

Blom, C., Forbes, D. A., Brodie, J. P., Foster, C., Romanowsky, A. J., Spitler, L. R.

and Strader, J., 2012, MNRAS, 426, 1959

• Chapter 5 has been submitted to MNRAS as:

“The SLUGGS survey: New evidence for a tidal interaction between the early type

galaxies NGC 4365 and NGC 4342”

Blom, C., Forbes, D. A., Foster, C., Romanowsky, A. J. and Brodie, J. P., 2013

Contributions to the papers from coauthors are included in this work for clarity and

continuity. All work is my own, unless a Section or piece of work is specifically attributed

to a coauthor by name.

Christina Blom

Melbourne, Victoria, Australia

2013

vi

Contents

Abstract i

Acknowledgements iii

Declaration v

List of Figures xi

List of Tables xv

1 Introduction 1

1.1 Globular clusters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Globular cluster systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2.1 Bimodality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.2.2 Trends within GC systems . . . . . . . . . . . . . . . . . . . . . . . . 4

1.2.3 GC system trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.3 Early-type galaxies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.4 Using globular cluster systems to study galaxy evolution . . . . . . . . . . . 7

1.5 Thesis outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2 Observing extragalactic globular clusters 9

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.2 Subaru/Suprime Cam Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.2.1 Observations and data reduction . . . . . . . . . . . . . . . . . . . . 11

2.2.2 Photometry and calibration . . . . . . . . . . . . . . . . . . . . . . . 11

2.3 HST/ACS Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.3.1 Observations and object measurement . . . . . . . . . . . . . . . . . 13

2.4 GC candidate Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.4.1 HST/ACS GCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.4.2 Subaru/SuprimeCam GCs . . . . . . . . . . . . . . . . . . . . . . . . 16

2.4.3 Contamination and completeness . . . . . . . . . . . . . . . . . . . . 19

2.4.4 Overview of GC selection . . . . . . . . . . . . . . . . . . . . . . . . 21

3 Wide-field imaging of NGC 4365’s globular cluster system 23

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.2 Analysis of the galaxy light . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

vii

viii Contents

3.2.1 Parameters of the isophotal fits . . . . . . . . . . . . . . . . . . . . . 25

3.3 Analysis of the GC System . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3.3.1 Characterising the total GC System . . . . . . . . . . . . . . . . . . 27

3.3.2 Radial colour gradients . . . . . . . . . . . . . . . . . . . . . . . . . 34

3.3.3 Colour-magnitude trends . . . . . . . . . . . . . . . . . . . . . . . . 36

3.3.4 Quantifying the GC subpopulations . . . . . . . . . . . . . . . . . . 38

3.3.5 Characterising the GC system subpopulations . . . . . . . . . . . . . 42

3.3.6 Comparison with galaxy surface brightness . . . . . . . . . . . . . . 53

3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

3.5 Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

4 Globular cluster system kinematics and substructure in NGC 4365 59

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

4.2 Spectroscopic sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

4.2.1 Data acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

4.2.2 Obtaining line-of-sight velocities . . . . . . . . . . . . . . . . . . . . 65

4.2.3 Low velocity GCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

4.3 Kinematics of the GC subpopulations . . . . . . . . . . . . . . . . . . . . . 71

4.3.1 Kinematic model description . . . . . . . . . . . . . . . . . . . . . . 71

4.3.2 Kinematics as a function of colour . . . . . . . . . . . . . . . . . . . 73

4.3.3 Dividing the sample into three subpopulations . . . . . . . . . . . . 77

4.3.4 Radial kinematics for three subpopulations . . . . . . . . . . . . . . 79

4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

4.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

5 Tidal interaction between NGC 4365 and NGC 4342 91

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

5.2 Photometric Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

5.2.1 Imaging Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

5.2.2 Globular cluster candidate selection . . . . . . . . . . . . . . . . . . 93

5.2.3 Spatial distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

5.2.4 Colour distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

5.3 Spectroscopic Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

5.3.1 Spectroscopic Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

5.3.2 Velocity selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

5.4 Kinematic properties of NGC 4342’s GC system . . . . . . . . . . . . . . . 111

Contents ix

5.5 Dark matter constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

5.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

5.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

6 Conclusions and future work 119

6.1 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

6.2 Avenues for Further Research . . . . . . . . . . . . . . . . . . . . . . . . . . 122

6.2.1 Substructure within galaxy groups and clusters . . . . . . . . . . . . 122

6.2.2 Searching for the causes of significant kinematic misalignment . . . . 123

6.3 Broad impact of this work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

Bibliography 129

A Statistical tests for trimodality 139

A.1 Kaye’s Mixture Model algorithm . . . . . . . . . . . . . . . . . . . . . . . . 139

A.2 The Kolmogorov-Smirnov test . . . . . . . . . . . . . . . . . . . . . . . . . . 142

A.3 Chi-Squared minimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

B Surface density data 145

B.1 Surface density data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

C Additional supopulation kinematic fits 147

C.1 Total subpopulation kinematic fits . . . . . . . . . . . . . . . . . . . . . . . 147

C.2 Radial kinematic fits with fixed position angle . . . . . . . . . . . . . . . . . 150

List of Figures

2.1 Three filter (g′, r′ and i′) image stack of NGC 4365 from the Subaru Tele-

scope SuprimeCam instrument. . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.2 Calibration of Subaru SuprimeCam photometry to Sloan Digital Sky Survey

filters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.3 Comparison of ACS sizes and magnitudes published in Jordan et al. (2009)

with those measured in this work. . . . . . . . . . . . . . . . . . . . . . . . . 14

2.4 Size in parsec vs. g − z colour distribution for GC candidates. . . . . . . . . 15

2.5 Colour magnitude diagram for GC candidates selected from HST/ACS

imaging. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.6 Diagram to show selection of point sources in the S-Cam imaging. . . . . . 17

2.7 Colour selection of GCs in the S-Cam field-of-view. . . . . . . . . . . . . . . 18

2.8 Colour magnitude diagram for GC candidates selected from S-Cam imaging. 19

2.9 Estimate of contamination in the S-Cam data. . . . . . . . . . . . . . . . . 20

2.10 Estimate of the completeness of the S-Cam photometric sample. . . . . . . 21

3.1 Isophotal parameters of NGC 4365 for g′, r′ and i′ filters. . . . . . . . . . . 26

3.2 Surface density of GC candidates plotted against galactocentric radius. . . . 28

3.3 Surface density of GC candidates in two dimensions from S-Cam data. . . . 30

3.4 Histogram of the azimuthal distribution of GC candidates. . . . . . . . . . . 31

3.5 GC density with colour and galactocentric radius for the ACS data. . . . . 32

3.6 GC density with colour and galactocentric radius for the S-Cam data. . . . 33

3.7 Rolling peak colour values plotted against galactocentric radius. . . . . . . . 34

3.8 The colour-magnitude relation for blue GC candidates. . . . . . . . . . . . . 37

3.9 Colour distribution of GC candidates at all radii. . . . . . . . . . . . . . . . 40

3.10 The normalised distribution of ACS GCs with colour in radial bins. . . . . . 41

3.11 The normalised distribution of S-Cam GCs with colour in radial bins. . . . 43

3.12 GC radial surface density for three subpopulations incorporating ACS and

S-Cam GCs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

3.13 The percentage of each subpopulation plotted against galactocentric radius. 46

3.14 The half light radius of bright HST/ACS GC candidates against galacto-

centric radius. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

3.15 The normalised mass function for GC candidates from the ACS catalogue. . 49

3.16 Azimuthal distributions of high probability GC subpopulations. . . . . . . . 51

3.17 Comparison of galaxy light surface brightness with GC surface density. . . . 52

xi

xii List of Figures

4.1 The positions of the six DEIMOS slitmasks plotted on the S-Cam image. . 60

4.2 Colour distribution of GCs with radial velocity measurements compared

with the total photometric sample. . . . . . . . . . . . . . . . . . . . . . . . 63

4.3 Line-of-sight velocity distribution for GCs in the kinematic sample. . . . . . 66

4.4 Line-of-sight velocity of GCs plotted against galactocentric radius. . . . . . 67

4.5 GC surface density with colour and galactocentric radius. . . . . . . . . . . 68

4.6 Spatial distribution of GCs showing individual radial velocities. . . . . . . . 70

4.7 Kinematics for NGC 4365 GCs as a function of g′ − i′ colour. . . . . . . . . 73

4.8 Observed line-of-sight velocity plotted against position angle for the three

GC subpopulations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

4.9 Kinematics as a function of radius for three GC subpopulations . . . . . . . 80

4.10 Kinematic parameters of GCs as a function of galactocentric radius. . . . . 82

4.11 Schematic representation of the NGC 4365 stellar light and GC system. . . 88

5.1 Spatial alignment of u and g filter imaging from the CFHT. . . . . . . . . . 94

5.2 Colour selection of globular cluster (GC) candidates. . . . . . . . . . . . . . 95

5.3 Spatial distribution of GC candidates around NGC 4342 and NGC 4365. . . 96

5.4 Radial surface density of GCs around NGC 4342. . . . . . . . . . . . . . . . 98

5.5 Azimuthal distribution of GCs around NGC 4342. . . . . . . . . . . . . . . 100

5.6 Simulated spatial distribution of the GCs around NGC 4365 and NGC 4342.102

5.7 Calculated stream overdensities for 1000 simulated spatial distributions. . . 103

5.8 Scaling between Subaru/SuprimeCam and CFHT/MegaCam colours. . . . . 104

5.9 Colour distribution of NGC 4342 and stream GCs. . . . . . . . . . . . . . . 105

5.10 Comparison of independent velocity measurements. . . . . . . . . . . . . . . 107

5.11 Phase-space diagram of GC velocity as a function of distance from NGC 4365.109

5.12 GC system velocity dispersion in three radial bins. . . . . . . . . . . . . . . 112

5.13 Imaging of NGC 4365 and other nearby galaxies in the W ′ group. . . . . . . 113

5.14 Circular velocity profile of NGC 4342. . . . . . . . . . . . . . . . . . . . . . 115

6.1 Reproduction of Figures 5.13 and 5.3. . . . . . . . . . . . . . . . . . . . . . 124

6.2 Reproduction of Figure 8a from Krajnovic et al. (2011). . . . . . . . . . . . 125

A.1 Bimodal and trimodal fits to the Epanechnikov kernel smoothing of the

ACS GC colour distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . 144

A.2 Bimodal and trimodal fits to the Epanechnikov kernel smoothing of the

S-Cam GC colour distribution. . . . . . . . . . . . . . . . . . . . . . . . . . 144

List of Figures xiii

C.1 Kinematics as a function of radius for the three GC subpopulations. . . . . 151

List of Tables

2.1 Imaging obtained from the Hubble Legacy Archive. The pointings are num-

bered to correspond with the numbers in Figure 2.1 and HST ID refers to

the HST proposal identification number where the principle investigators

are as follows: 9401 - Patrick Cote, 10584 - Gregory Sivakoff, 9488 - Kavan

Ratnatunga. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.1 Values for g, r and i Sersic profiles fitted to galaxy light. . . . . . . . . . . . 25

3.2 Fitted values of the Sersic fit to the GC surface density. . . . . . . . . . . . 29

3.3 GC colour gradients for the linear fit to colour with radius in arcmin. . . . 35

3.4 GC colour gradients for the logarithmic fit to colour with normalised radius. 35

3.5 Metallicity gradients for the logarithmic fit to colour with normalised radius. 36

3.6 Gaussian values for the blue and red GC distributions. . . . . . . . . . . . . 39

3.7 Sersic profile fits to the radial surface density of GC subpopulations. . . . . 45

3.8 Estimated ellipticity for blue, green and red GC subpopulations. . . . . . . 52

4.1 Summary of kinematic fits to various GC subpopulation divisions. . . . . . 75

4.2 Summary of the NGC 4365 galaxy system properties. . . . . . . . . . . . . 83

5.1 Recession velocities for GCs around NGC 4342 and in the stream. . . . . . 110

A.1 Results from the extended KMM test for significance of m modes. . . . . . 141

A.2 KS test results comparing galaxies in the ACS Virgo Cluster Survey. . . . . 142

A.3 KS test results comparing simulated distributions with NGC 4365’s colour

distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

B.1 GC radial surface density data for the total GC system and each subpopu-

lation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

C.1 Kinematic fits to various divisions of the three GC subpopulations. . . . . . 149

C.2 Minimisation parameter divided by degrees of freedom for kinematic fits. . . 150

xv

1Introduction

The heavens declare the glory of God;

the skies proclaim the work of his hands.

Day after day they pour forth speech;

night after night they reveal knowledge.

They have no speech, they use no words;

no sound is heard from them.

Yet their voice goes out into all the earth,

their words to the ends of the world.

—Psalm 19:1-4a

Since the discovery that the Universe contains galaxies separate from our own (Hubble,

1926), scientists have been fascinated by the question of how they came to be. Once it

was discovered that galaxies are not all spiral in structure, like our own Milky Way and

the Andromeda galaxy, but that some have elliptical shapes, like M32 and NGC 404, the

question of whether they formed differently or evolved into different types of galaxies,

was also raised. Spiral (late-type) and elliptical (early-type) galaxies have many very

different properties, but large galaxies (both spiral and elliptical) all host globular clusters

(GCs). In this thesis we use GCs and the properties of a GC system to answer some

of the current questions of galaxy evolution. We will consider the case of an intriguing

giant elliptical galaxy, NGC 4365. Here we introduce the properties of individual GCs,

current understanding of GC systems, salient aspects of elliptical galaxies and then discuss

the ways in which GC systems can be used to study questions of galaxy formation and

evolution.

1

http://www.biblegateway.com/passage/?search=Psalm+19&version=NIV

2 Chapter 1. Introduction

1.1 Globular clusters

Globular clusters are very dense clusters of stars, gravitationally bound to within 2 - 10

pc in radius (van den Bergh et al., 1991; Harris, 1991). Unlike other stellar systems GCs

contain very little gas and no dark matter. The discovery of GCs began in the mid 17th

Century when Abraham Ihle spotted the perfectly round nebula-like M22, but he could

not resolve the GC into stars. Only in the 1780’s when William Herschel was doing a

survey of many of these round nebulae were they first termed globular clusters (Herschel,

1786, 1789).

The internal properties of Milky Way GCs have been studied in great detail since those

first discoveries. Studies of the photometric colours and chemical properties of individual

stars in GCs have shown that, to first order, the stars within a single GC formed at the

same time, from the same original gas cloud. Using resolved stellar photometry and an

understanding of stellar evolution, the horizontal giant branch turnoff (Hesser et al., 1987)

or main sequence turnoff (Gratton, 1985) have been used to age date most of the GCs

in the Milky Way. It has been found that most of the GCs in the Milky Way are > 10

Gyr old (Marın-Franch et al., 2009). Spectroscopic studies have confirmed that the mean

[Fe/H] metallicity of the stars in a Milky Way GC range from 0.0 to -2.3 (Harris, 2010).

The picture of GCs as old stellar systems that were formed early in the evolution of the

Universe from low-metallicity gas, has been confirmed by detailed studies of GCs in other

galaxies (see Strader et al., 2005, and references therein).

Very recent studies of resolved stars in Milky Way GCs have revealed that some GCs

actually host multiple stellar populations (Bedin et al., 2004; Piotto et al., 2007; Milone

et al., 2008). This was determined from the presence of multiple turnoff points for the

main sequence of stellar evolution. However, for the GCs where this has been seen the

age difference between the stellar populations is < 1 Gyr (Gratton et al., 2012). There

is debate as to the formation processes for multiple stellar populations within GCs (e.g.

Decressin et al., 2007; Conroy & Spergel, 2011; Valcarce & Catelan, 2011) but for GCs

where the stars cannot be individually resolved (i.e. for galaxies outside the Local Group)

it is reasonable to view GCs as single stellar population systems that have survived from

the early Universe.

1.2 Globular cluster systems

In 1932 Edwin Hubble presented a catalogue of ‘Nebulous objects in Messier 31 provision-

ally identified as globular clusters’ identified from photographic plates (Hubble, 1932).

1.2. Globular cluster systems 3

This marked the start of GC system studies outside the Milky Way. That first study used

many of the photometric techniques we use to study extragalactic GC systems today (e.g.

measures of the radial distribution and most common GC magnitude), however many new

techniques have also been added to the field over the last 80 years. We present a summary

of the salient discoveries of GC system research since the publication of Edwin Hubble’s

catalogue.

1.2.1 Bimodality

Zepf & Ashman (1993) discovered that the GC systems of the giant elliptical galaxies

NGC 5128 and NGC 4472 are both bimodal in optical colours. Within a decade it was

clear that the GC systems of most giant elliptical galaxies are bimodal and that GC

bimodality is common in large spiral galaxies too (Forbes et al., 1997; Larsen et al., 2001;

Kundu & Whitmore, 2001). Given the degeneracy between age and metallicity in optical

colours is was not initially clear whether GC systems were bimodal in metallicity or in

age. Observations of Milky Way GCs had shown them to be > 10 Gyr old and if both

modes/subpopulations had the same metallicity a 13 Gyr age gap would be required to

explain the observed gap in colour between the two subpopulations (Forbes et al., 1997).

It was generally assumed that all the GCs in a galaxy were > 10 Gyr old, roughly coeval

and with different intrinsic metallicity, referring to a blue metal-poor subpopulation and

a red metal-rich subpopulation. This paradigm was challenged by Yoon et al. (2006)

who postulated that while GCs are likely old and roughly coeval they are not bimodal

in metallicity. Rather, that there is a continuous distribution of GCs from metal-poor to

metal-rich and the observed bimodality in optical colours is due to a highly non-linear

relationship between intrinsic GC metallicity and observed GC colour (see also Cantiello

& Blakeslee, 2007; Blakeslee et al., 2010). Recent observations of GC metallicity, using

various spectroscopic indices, have shown that many GC systems are bimodal in metallicity

and that the relationship between GC colour and GC metallicity is well approximated with

a linear relation (Strader et al., 2007; Woodley et al., 2010; Alves-Brito et al., 2011; Usher

et al., 2012).

Since the discovery of GC system bimodality various people have researched the differ-

ent properties of each GC subpopulation to investigate their possible origins. They found

that while there is a large scatter in GC sizes, the mean size of blue GCs is larger than

the mean size of red GCs (Larsen & Brodie, 2003; Jordan, 2004). It was also determined,

through fitting de Vaucouleurs and Sersic profiles to the radial density of GC subpopu-

lations, that blue GC subpopulations are generally flatter and extend further from the


galaxy centre than red GC subpopulations (Harris, 2009b; Pota et al., 2013). It is also

possible to do kinematic studies of GC systems. These studies require secure recession

velocities for a large number of GCs in each subpopulation and the sample of galaxies

where this analysis has been possible is still growing. Early results show that the red GC

subpopulation of large galaxies generally have similar kinematic properties to the galaxy

stellar light, while the blue GC subpopulations either have kinematic properties that are

decoupled from that of the red subpopulations or are dominated by random motions (e.g.

Cote et al., 2001, 2003; Lee et al., 2010; Arnold et al., 2011; Foster et al., 2011; Pota et al.,

2013).

Bimodality in GC systems implies that there are significant differences in the forma-

tion of blue and red GCs. There are various interpretations of the formation differences;

perhaps blue GCs formed at a different time to red GCs (Forbes et al., 1997), perhaps

blue and red GCs were formed via different physical processes (Ashman & Zepf, 1992) or

perhaps they were formed in very different environments (Cote et al., 1998). It is thought

that GCs formed during intense periods of general star formation over the history of galaxy

evolution (Larson, 1990) and consequently the conclusions we draw regarding the differ-

ences in formation of blue and red GC subpopulations extend to our understanding of the

formation of their host galaxies.

1.2.2 Trends within GC systems

The study of GC systems has revealed several relationships between the properties of GCs

within each galaxy. For example, the mean GC size (usually measured from the half-

light radius of the GCs) increases with increasing galactocentric radius. This size-radius

relationship has been measured in several galaxies (the Milky Way: van den Bergh et al.

1991, the brightest eight Virgo ellipticals: Jordan et al. 2005, NGC 4594: Spitler et al.

2006a, NGC 5128: Gomez & Woodley 2007, and a sample of 6 galaxies together: Harris

2009a) and with slopes varying from 0.05 to 0.36 dex dex−1. It has been suggested that this

apparent relationship arises due to a combination of two properties of the blue and red GC

subpopulations, namely that blue GCs tend to be larger in size than red GCs and blue GC

subpopulations tend to be radially more extended than red GC subpopulations (Jordan

et al., 2005; Masters et al., 2010). The argument being that smaller, red GCs dominated

the central regions and larger, blue GCs dominated the outer regions of galaxies, while

others (e.g. Harris, 2009a) argued that the radial trend of increasing GC size was due to

differences in the GCs interaction with the galaxy potential well at different radii.

1.2. Globular cluster systems 5

Spitler et al. (2006a) also discovered a relationship between the magnitude and the

colour of GCs in the blue GC subpopulation of the Sombrero galaxy (NGC 4594) and

called it a ‘blue-tilt’. It was seen soon after in several other galaxies (Strader et al.,

2006; Mieske et al., 2006; Harris et al., 2006). This colour-magnitude, or mass-metallicity

relationship has been attributed to the self-enrichment of GCs, whereby larger GCs are

better able to retain the gas ejected by fast lived stars to create more metal rich stars

(Strader & Smith, 2008; Bailin & Harris, 2009). Several galaxies show a blueward trend of

mean colour with increasing galactocentric radius of one or both GC subpopulations (see

Forbes et al., 2011, and references therein). One or both GC subpopulations of NGC 1407

(Forbes et al., 2011), NGC 4472 (Geisler et al., 1996), NGC 1399 (Bassino et al., 2006),

M87 (Harris, 2009b), NGC 3268, NGC 4696 and NCG 7626 (Harris, 2009a) have mean

metallicities that are more metal rich in the outer parts than the inner parts. The cause of

this trend is not yet known but Forbes et al. (2011) suggest that it arises due to dissipative

processes when stars and GCs are formed in situ.

1.2.3 GC system trends

To understand the connection between GC systems and their host galaxies it is important

to compare the GC system properties of various different galaxies and identify trends. It

became clear relatively early that the luminosity function of the GCs is almost identical

across all the observed GC systems (Hanes, 1977; Hanes & Whittaker, 1987). The log-

normal distribution of GC luminosity peaks at Mz = −8.4 mag (Jordan et al., 2007) in

every GC system that has been studied and the universality of this value has been used

to determine galaxy distances in several cases (see the review by Jacoby et al. 1992 and

a modification by Ashman et al. 1995). The specific frequency, a measurement of the

number of GCs normalised by the luminosity of the host galaxy (Harris & van den Bergh,

1981), does vary with host galaxy property. SN plotted against host galaxy luminosity

forms a horse-shoe shape (see Forbes, 2005; Peng et al., 2008), low for intermediate mass

galaxies and increasing with both decreasing and increasing luminosity. The relationship is

highly scattered at low luminosities, as these galaxies are difficult to observe and their GC

numbers are low, but the relationship of increasing SN with increasing galaxy luminosity

is well defined. In general, larger/brighter galaxies host more GCs per unit luminosity

than fainter/smaller galaxies.

Several researchers over the last decade have found that the properties of the red GC

subpopulation correlate well with the properties of the host galaxy. Both the colour and

number of red GCs in a GC system show a trend with host galaxy luminosity (Peng et al.,


2006) and the mean colour of red GCs is often similar to the colour of the galaxy starlight

(Forbes & Forte, 2001). These results support the hypothesis that the red subpopulation

of GCs are associated with the bulge stars of galaxies (Forbes et al., 1997; Forbes & Forte,

2001), while the blue GC subpopulation is likely associated with the halo of galaxies.

Recent investigation has found that for a number of galaxies the blue GC radial surface

density is similar to the hot gas density profile as measured from X-ray emission (Forbes

et al., 2012).

1.3 Early-type galaxies

The primary feature of early-type (lenticular and elliptical) galaxies is that their current

rate of star formation is generally very low. Viewed in optical colours they appear red

because they do not contain many young blue stars, but mainly older yellow and red

stars. They also generally do not host significant amounts of neutral hydrogen nor do

giant ellipticals have strong rotation profiles, although it seems low luminosity ellipticals

rotate more significantly (Emsellem et al., 2007). Elliptical galaxies are often found in

the very centre of the largest clusters of galaxies and the very largest observed galaxies

are elliptical (e.g. M87 and NGC 1399). There has been significant debate about whether

spiral galaxies become lenticular or elliptical galaxies when they are starved of gas and/or

merge with other galaxies (Kauffmann et al., 1993; Cole et al., 1994; Baugh et al., 1996;

Navarro et al., 1997), or if elliptical galaxies were formed without coherent rotation and

have since just been starved of the gas used for original star formation (Eggen et al., 1962;

Larson, 1974; Carlberg, 1984; Arimoto & Yoshii, 1987). The lack of neutral hydrogen

presents a significant hurdle in attempts to study the internal structure or dark matter

distribution of these galaxies and thus it is very difficult to disentangle the evolutionary

history of elliptical or lenticular galaxies.

Daddi et al. (2005) discovered superdense or ‘red nugget’ galaxies in the early universe

and as a result a new scenario for elliptical galaxy formation has emerged. It is suggested

that elliptical galaxies evolve from the red nuggets in the early universe, likely formed

through dissipative collapse into dark matter overdensities. To evolve into the giant passive

galaxies seen today the ‘red nuggets’ accrete many small galaxies, gaining a little bit of

mass but growing significantly in radial extent due to the frequent, minor tidal events (van

Dokkum et al., 2008). This two-phase growth of galaxies, with early dissipative collapse

and subsequent minor mergers, is also supported by the results of simulations such as

Naab et al. (2009).

1.4. Using globular cluster systems to study galaxy evolution 7

1.4 Using globular cluster systems to study galaxy evolution

Perhaps the first example of using GC systems to study the fundamental parameters of

galaxies is the work of Harlow Shapley using GCs in the Milky Way to determine the size

and centre of our own Galaxy (Shapley, 1918). He mapped the positions and distances

of all the known Galactic GCs to form a 3D model, finding that the GCs roughly filled a

sphere and that the sphere was not centred on our own solar system but a point roughly

15 kpc away from us. He concluded that the sun is not at the centre of the Milky Way

and that our Galaxy is much larger than other measurements at the time predicted.

With modern observing techniques it is possible to investigate intrinsic GC colours or

metallicities as well the kinematic properties of the GC subpopulations for many galaxies

and compare the results (e.g. Usher et al., 2012; Pota et al., 2013). We can use these

detailed studies of GC system properties to disentangle the evolutionary history of their

host galaxies. Additionally, because GC systems are much more extended than the un-

derlying galaxy light, GC systems are often used to trace the dark matter distribution

around elliptical galaxies (e.g. Romanowsky et al., 2009).

The discovery of universal GC system bimodality has had a major impact on the study

of galaxy evolution. Because the blue and red GC subpopulations have been shown to be

distinct in metallicity space (Usher et al., 2012) it follows that they have formed separately.

If GC formation is associated with episodes of major star formation (Larson, 1990), uni-

versal GC bimodality suggests that the stars seen in galaxies were overwhelmingly formed

during two episodes. This seems to contradict the continual star formation that is pre-

dicted by the ΛCDM model (Blumenthal et al., 1984; Davis et al., 1985; Navarro et al.,

1997). However, more subtle implementations of ΛCDM modelling with GC formation in-

cluded (e.g. Muratov & Gnedin, 2010; Tonini, 2013) indicate that GC system metallicity

bimodality can be reproduced by hierarchical clustering. Muratov & Gnedin (2010) and

Tonini (2013) differ in the processes of red and blue GC formation, using galaxy mergers

to trigger GC formation, and using the measured peak of cosmic star formation as an

indicator for GC formation, respectively. There are still many questions to be answered

about the formation of elliptical galaxies and their GC systems.

Here, we look at the specific case of a giant elliptical galaxy where a third GC subpop-

ulation is suspected. This investigation could provide the key to understanding the causes

of universal bimodality, by identifying the origin of the additional GC subpopulation in

NGC 4365.


1.5 Thesis outline

Chapter Two presents the photometric data, the starting point for the investigation of the

‘Globular Cluster System of NGC 4365’. We present techniques to discover GCs at a large

distance from the observer and contrast the process of identifying GCs with ground based

and with space based imaging. The data analysis and discussion are presented in Chapter

Three, characterising the total GC system in Section 3.3.1 followed by an argument for

the division of the GC system into three separate subpopulations in Sections 3.3.2 - 3.3.4

and analysis of the properties of each GC subpopulation in Section 3.3.5. We analyse

the kinematic properties of the GCs of NGC 4365 in Chapter Four. Section 4.2 presents

the spectroscopic data and recession velocity distribution while Section 4.3 contains the

kinematic analysis of each GC subpopulation in NGC 4365.

The analysis and discussion in Chapter Five illuminate the process of tidal stripping

that is happening in the galaxy group around NGC 4365. This is done using both photo-

metric (Section 5.2) and spectroscopic (Sections 5.3 and 5.4) studies. In Chapter Six we

summarise the main results and arguments from Chapters Two to Five, discuss possible

avenues for future research and conclude with a comment on the broader impact of the

discoveries presented in the thesis.

2Observing extragalactic globular clusters

A scientist in his laboratory is not a mere technician: he is also a

child confronting natural phenomena that impress him as though

they were fairy tales.

—Prof. Marie Skodowska-Curie

2.1 Introduction

The observation of globular clusters (GCs) in our own galaxy is fundamentally different

from studies of GCs in external galaxies at the distance of the Virgo Cluster. With modern

instruments a large percentage of the stars in Galactic GCs can be individually resolved

for photometric and spectroscopic measurement. This means that galactic GCs can be

unambiguously identified as such, without risking confusion between GCs, stars, star-

forming regions or background galaxies. Probing out to the distance of the Virgo Cluster it

is no longer possible to measure internal chemical, dynamical or evolutionary properties for

each GC by resolving and analysing each star. It is however possible to obtain information

about the GC from the integrated properties of all its stars combined in one observation.

The difficulty with observing GCs when individual stars cannot be resolved starts with

the problem of unambiguously determining if the object is actually a GC. We will discuss

the photometric methods currently used to select a sample of objects that is dominated

by GCs. These techniques are different depending on whether ground-based observations

or space based observations are available. The data set obtained for the giant elliptical

galaxy NGC 4365 (a member of the W ′ group behind the Virgo Cluster) contains imaging

from the Suprime-Cam instrument on the ground-based 8m Subaru telescope (Subaru/S-

Cam) as well as from the Advanced Camera for Surveys on the Hubble Space Telescope

9

http://en.wikipedia.org/wiki/Marie_Curie

10 Chapter 2. Observing extragalactic globular clusters

Figure 2.1: The central 34 × 25 arcmin section of the Subaru/S-Cam g′, r′ and i′ filtercombined image (trimmed from 35× 27 arcmin for cosmetic purposes) and the footprintsof the 8 HST/ACS pointings are shown. The scale of the ACS pointings is shown in thebottom left corner. Image is centred on α=12:24:26.824; δ=+07:19:03.52 (J2000.0). At adistance of 23.1± 0.8 Mpc (Blakeslee et al., 2009) 1arcsec = 0.112 kpc.

(HST/ACS).

While observations of the individual GC properties are difficult to do for a galaxy at

the distance of NGC 4365, observations of the global properties of its GC system are more

easily done because it is a giant elliptical galaxy external to the Milky Way. We can see

the whole of NGC 4365, as apposed to missing all the Milky Way GCs hidden by the dense

centre of our Galaxy. The other benefit of studying the GC systems of elliptical galaxies

is that they generally contain little or no dust, that obscures many GCs on the far side of

the Andromeda galaxy for example.

In Section 2.2 and 2.3 we overview the data acquisition and reduction for the Subaru/S-

Cam and HST/ACS photometry respectively. We describe the GC candidate selection

criteria and detail the photometric properties of the GC candidate samples in Section 2.4.

2.2. Subaru/Suprime Cam Data 11

2.2 Subaru/Suprime Cam Data

2.2.1 Observations and data reduction

On 2008, April 1st we obtained 35 × 27 arcmin three-filter imaging of NGC 4365 using

Subaru/S-Cam. Conditions were good and the worst seeing was ∼ 0.8 arcsec, exposure

times were 5× 130 s, 5× 70 s and 5× 60 s for g′, r′ and i′ filters respectively. The images

were bias subtracted, flat field corrected and stacked using the SDFRED package (Yagi

et al., 2002; Ouchi et al., 2004) and put onto the USNO-B2 astrometric system. Figure

2.1 shows a combination of the g′, r′ and i′ filter images. The S-Cam instrument has a

pixel scale of 0.202 arcsec.

The NGC 4365 galaxy light was modelled and subtracted in each of the three filters

using the IRAF task ELLIPSE before an object detection algorithm was employed. This

was done to increase the success with which the DAOFIND detection algorithm finds

faint objects in the central regions of the galaxy (it is unsuccessful at finding objects in

areas in which the background surface brightness varies). The standard deviation of the

background, after galaxy subtraction, was σ = 8.64 counts in g′, σ = 11.16 counts in r′

and σ = 13.15 counts in i′. Detection thresholds of 2.7σ, 3.0σ and 2.1σ in g′, r′ and i′

respectively were used so as to probe as deep as possible in all areas of the images, with the

assurance that the selection criteria employed later (see Section 2.4.2) would remove most

spurious detections (due to cosmic rays, background fluctuations and galaxy subtraction

artifacts).

2.2.2 Photometry and calibration

The point-like objects (see Section 2.4.2 for details on point source determination) brighter

than i′ = 22 were used to define an optimum aperture for which to extract the photometry.

The photometry was extracted at a radius of 3.5 pixels in g′ and 3.0 pixels in r′ and i′

(the seeing was ∼ 0.6 in r′ and i′ but ∼ 0.8 in the g′ filter). The radius of extraction

was chosen as a trade-off between increasing error from background noise and increasing

uncertainty in the correction for light outside the extraction aperture (i.e. an aperture

correction). The aperture correction was −0.382± 0.016 mag in g′, −0.263± 0.012 mag in

r′ and −0.249± 0.011 mag in i′. The sky value was calculated as the mode of all pixels in

an annulus between 15 and 20 pixels from the centre of the object. The standard deviation

of the pixel values in the sky subtraction annulus was included in the calculation of the

error for each individual source.

Standard stars were not observed with NGC 4365, instead the photometry was cali-


Figure 2.2: Calibration of S-Cam photometry to SDSS filters. The difference betweenzeropoint corrected S-Cam and SDSS magnitudes for g′, r′ and i′ filters are plotted fromtop to bottom. (Left) The residuals are plotted against the (g′− i′) colour and the colourrange is restricted to that expected for GCs. All three filters show asymmetry in theirdistributions, scattering to brighter S-Cam magnitudes. (Right) The residuals are shownagainst SDSS magnitude.

brated using bright point-like objects (18 < i′ < 22) detected in the NGC 4365 field that

were also found in the Sloan Digital Sky Survey (SDSS) catalogue. Both PSF and model

derived SDSS magnitudes were used for this and no systematic difference was found in

the results. There were 1822, 2174 and 2813 cross matched objects for calibration in g′,

r′ and i′ filters respectively and the photometric calibration zeropoints were determined

from a best fit linear relation between the SDSS catalogue magnitudes and the S-Cam

instrumental magnitudes. These were found to be zpg′ = 27.64± 0.03, zpr′ = 27.76± 0.08

and zpi′ = 27.72± 0.10 on the AB photometric system. The errors in the zeropoints were

determined by adding in quadrature the errors from the SDSS photometry and the errors

from the S-Cam photometry and taking the mean. Across all magnitudes the root mean

square of the differences between SDSS and S-Cam photometry are 0.36, 0.37 and 0.38

for the g′, r′ and i′ filters respectively. The zeropoint corrected residuals of point-like

objects in each filter are shown in Figure 2.2, plotted against g′− i′ colour (left) and filter

magnitude (right). The SDSS and S-Cam g′, r′ and i′ filters show good agreement with

no obvious systematic colour or magnitude trend.

It was necessary to correct for foreground dust extinction as such a correction was not

applied to the SDSS calibration objects. We used the Schlegel et al. (1998) dust maps to

calculate the extinction correction in each filter. The values were compared at different

positions across the field and not found to vary significantly. For g′, r′ and i′ filters the

extinction correction values used were Ag′ = 0.081, Ar′ = 0.060 and Ai′ = 0.045 mag.

2.3. HST/ACS Data 13

Central Exposures HSTR.A. Dec. Filter Time (s) ID

F475W 7501 12:24:27.0 07:19:20.8 F850LP 1210 9401

F475W 6802 12:24:17.5 07:21:09.5 F850LP 1170 10582

F475W 6803 12:24:16.0 07:17:37.1 F850LP 1170 10582

F475W 6804 12:24:26.3 07:15:44.8 F850LP 1170 10582

F475W 6805 12:24:36.4 07:17:25.1 F850LP 1170 10582

F475W 6806 12:24:41.3 07:20:18.2 F850LP 1170 10582

F475W 6807 12:24:31.5 07:22:20.4 F850LP 1170 10582

F475W 17448 12:24:07.2 07:12:11.3 F775W 1624 9488

Table 2.1: Imaging obtained from the Hubble Legacy Archive. The pointings are num-bered to correspond with the numbers in Figure 2.1 and HST ID refers to the HST proposalidentification number where the principle investigators are as follows: 9401 - Patrick Cote,10584 - Gregory Sivakoff, 9488 - Kavan Ratnatunga.

Hereafter we quote extinction corrected magnitudes and colours.

2.3 HST/ACS Data

2.3.1 Observations and object measurement

We obtained, from the Hubble Legacy Archive, g and z (g and i for one pointing) filter

imaging for eight separate pointings of the HST/ACS instrument around NGC 4365. Table

2.1 summarises these observations and Figure 2.1 shows the ACS pointing footprint on the

S-Cam image. These archival data probe down to z = 25.2, at a 50 per cent completeness

level (Jordan et al., 2007). The resolution of HST/ACS data (pixel scale is 0.05 arcsec)

partially resolves GCs at the distance of NGC 4365, 23.1 Mpc away (Blakeslee et al.,

2009).

The eight individual ACS fields were analysed using a custom built pipeline (see e.g.

Strader et al. 2006 and Spitler et al. 2006b) to find small, round objects and measure their

magnitudes and half light radii (henceforward referred to as object size). The pipeline

was run by co-supervisor Dr Lee Spitler. For details on the methods used by the pipeline


Figure 2.3: Comparison of ACS sizes and magnitudes published in Jordan et al. (2009)with those measured in this work. The difference between the g (blue) and z (red) mag-nitudes is plotted in the left panel and the differences between the sizes (in arcseconds) isplotted in the right panel.

including point spread function determination see Strader et al. (2006).The object lists

from each field were collated, with the arithmetic mean taken of the sizes and magnitudes

in the field overlap areas, and associated errors adjusted to reflect the more accurate mea-

surement. The median standard deviation between the measured magnitudes in overlap

areas is 0.04 mag and the median standard deviation in the measured sizes is 0.25 pixels

(1.4 parsec).

In Figure 2.3 the measured magnitudes and sizes are compared with those published

for objects in the central field (Jordan et al., 2009). The magnitude measurements show no

evidence of a statistically-significant systematic offset from the published data. The scatter

increases to fainter objects but is on the order of 0.1 magnitudes. The size measurements

in this work show a small ∼ 0.005 arcsec systematic offset to smaller sizes attributed to

differences in size measurement techniques.

2.4 GC candidate Selection

2.4.1 HST/ACS GCs

GC candidates were selected from the HST/ACS images based on their size, colour and

magnitude. The GC candidate distribution is selected to have colours 0.7 < g − z < 1.6

and sizes 0.1 pc < rh < 20 pc. The choice of g − z colour upper and lower bounds was

based on the colour size diagram shown in Figure 2.4. There is a clear drop off in the

density of objects bluewards of 0.75 and a similar but less clear drop off redwards of 1.6

2.4. GC candidate Selection 15

Figure 2.4: Size in parsec vs. g − z colour distribution for GC candidates brighter thanthe turnover magnitude of z = 23.4, detected in the HST/ACS fields. The GC candidateselection criteria is marked with a box. The blue GC candidates tend to have largeraverage sizes and the red GC candidates have a larger dispersion in colour.

Figure 2.5: Colour magnitude diagram for GC candidates selected from HST/ACS imag-ing.


(−2.4 < [Fe/H] < 0.18 using the empirical transformation of Peng et al. (2006)). While

most of the GC candidates in Figure 2.4 are resolved, the redder GC candidates show

a size distribution that overlaps with objects of zero size (objects indistinguishable from

stellar point sources). A size cut at 0.1 pc (10−4 arcsec) was made because at smaller sizes

the objects did not show a distribution that was centrally concentrated on the galaxy and

therefore does not sample mainly GCs. A generous upper bound on size of 20 pc (0.2

arcsec) was employed to exclude background galaxies but include possible Ultra Compact

Dwarfs (UCDs) in the system. Lastly, only objects fainter than z = 19 (Mz = −12.8 mag)

are considered GC candidates of NGC 4365, following the convention of the ACS VCS

published catalogue (Jordan et al., 2009). The catalogue contains no objects fainter than

z = 26.2 mag, see Figure 2.5. Objects brighter than z = 19 are most likely to be stars.

2.4.2 Subaru/SuprimeCam GCs

Point source determination

At the distance of NGC 4365, GCs are not resolved by our Subaru observations and

consequently they cannot be separated from stars in our Galaxy via a size distinction. By

selecting only objects that are unresolved in the S-Cam imaging background galaxies are

excluded from the analysis of NGC 4365’s GC system. Here the distinction between a

point-like object (mostly stars and NGC 4365 GCs) and an extended object (galaxies) is

determined by a measure of the flux difference between two aperture radii. Shown in Figure

2.6 are the objects that were identified to be point-like in all three filters. Magnitudes were

extracted for two apertures different in radius by a half or full pixel (filter dependent) both

centred on the object. Extended objects have extra light in the larger aperture compared

to point-like objects.

The lists of point-like objects for each filter were cross-matched in position and only

objects classed as point-like in all three filters were kept in the sample. This was done

using the IRAF task TMATCH with a tolerance in positional offset between images of 1.26

arcsec (this was determined by the uncertainty in astrometry at the image edges). Because

the photometry is deeper in the i′ filter than either r′ or g′ filters this procedure likely

excluded genuine faint GCs from the analysis that were detected in i′ but not in either

of the other filters. Using a cross-matching technique spurious detections (due to cosmic

rays etc.) in any of the three filters were eliminated and only objects with photometry in

all three filters selected.


Figure 2.6: The difference in flux between two aperture radii (the smaller of which is theextraction aperture) vs. aperture corrected magnitude at the extraction aperture for allobjects found by the DAOFIND detection algorithm. Objects determined to be point-likein all three S-Cam filters are shown in black dots, overlapping the total object detectionin the g′ filter (grey dots). The dotted line shows the cutoff between point-like objectsand extended objects in the g′ filter. Bright point-like objects have aperture differencesof ∼ 0.1 magnitudes but fainter objects show significant scatter in measured magnitudes.A difference in magnitude between the inner and outer aperture of up to ∼ 0.6 mag isrequired to include fainter point-like objects.


Figure 2.7: Colour selection of GCs in the S-Cam field-of-view based on the r′ − i′ vs.g′−r′ colour of matched objects in the HST/ACS data. The objects shown are all brighterthan i′ = 22 mag and have magnitude errors smaller than 0.02 for visual clarity. S-Camdetected point-like objects are plotted in black plusses and HST/ACS matched objectsare overplotted as red dots. The HST/ACS matched objects define a very tight sequence(black line) that is used to define the area from which S-Cam objects are selected as GCcandidates (red box).

Colour-colour and magnitude selection

As shown in Figure 2.4 the HST/ACS is able to partially resolve GCs at the distance of

NGC 4365 and can therefore directly distinguish between unresolved foreground stars, GCs

and resolved background galaxies. We used the central HST/ACS pointing to determine

where on a S-Cam r′ − i′ vs. g′ − r′ diagram the GC sequence lies by matching S-Cam

point source detections with ACS GC candidates. A line was fitted to the brightest S-Cam

objects found to be GC candidates in the ACS field and used to make a box, 2σ in each

direction from the best fit GC sequence line. Objects were considered to be consistent

with the GC colour-colour definition if the standard error in their photometry placed them

within the box (see Figure 2.7). The selected distribution contains objects with colours

0.6 < g′ − i′ < 1.4, corresponding to metallicities of −2.1 < [Fe/H] < 0.7 when the Lee,

Park & Hwang (2010) empirical transformation is used. The blue (g′ − i′ ∼ 0.8) GCs lie

nearer the bottom left and red (g′ − i′ ∼ 1.2) GCs lie nearer the top right of the box in

Figure 2.7. Although blue GCs are generally more concentrated than red GCs this does

not affect the range in g′ − i′ of the selection box that should be used when the central


Figure 2.8: Colour magnitude diagram for GC candidates selected from S-Cam imaging.

GCs define the box, but this does affect the distribution of GCs in the box at different

radii. For example, the HST/ACS selected GCs in Figure 2.7 extend to the bluest colours

but are very sparsely distributed there, whereas the S-Cam selected GC candidates much

more densely populate the blue end. This is because the S-Cam imaging extends to much

larger radii.

An upper limit on the magnitude of any GC candidate was set to i′ = 19 (Mi′ = −12.8

mag) by the brightest GC found by HST/ACS in the central field of NGC 4365. A limit

of 0.1 mag in photometric error resulted in a lower limit of i′ ≈ 25 mag on the blue end of

the colour-colour selection and i′ ≈ 24.5 mag on the red end of the colour-colour selection.

The resulting colour-magnitude diagram (CMD) is seen in Figure 2.8.

2.4.3 Contamination and completeness

At galactocentric radii smaller than 0.9 arcmin S-Cam detection numbers drop due to

galaxy subtraction artifacts and at radii greater than 3.4 arcmin ACS detections are no

longer spatially complete due to the tiling pattern of HST/ACS pointings. Therefore only

in an annulus between 0.9 and 3.4 arcmin from the centre of NGC 4365 is it possible to

compare the object detections and photometry for the S-Cam and ACS imaging. This was

done to determine the level of contamination in the S-Cam GC sample (contamination is

defined as non GC objects that meet the colour, size and magnitude conditions set for the

S-Cam photometry) as well as to determine the photometric completeness of our S-Cam


Figure 2.9: Estimate of contamination in the S-Cam data by comparison with HST/ACSmatched objects. The solid line shows the number of S-Cam GCs in each magnitude bin,the dashed line shows the number of those objects that are determined to be contaminantsby HST/ACS imaging and the dotted line shows the percentage contamination of the S-Cam GC sample as a function of magnitude.

imaging. This analysis is relative to the HST/ACS sample but the ACS imaging is almost

perfectly complete several magnitudes deeper than our Subaru/S-Cam imaging (Jordan

et al., 2009).

The contamination percentage of the sample in S-Cam was determined by keeping

record of the GC candidates detected in the ACS imaging but removed by the conditions

outlined in Section 2.4.1. At a magnitude of z = 24.2, 50 per cent of S-Cam GC candidates

are determined to be contamination and at z = 24.5 mag essentially all of the S-Cam GC

candidates are contaminants, see Figure 2.9. Across the whole GC candidate sample the

contamination determined in this way is 4.14 ± 0.35 objects arcmin−2 and if the sample

is restricted to only objects brighter than z = 23.4 mag this value drops to 1.23 ± 0.19

objects arcmin−2. The luminosity function of GCs can be well described by a Gaussian

distribution peaked at the turnover magnitude of Mz ≈ −8.4 mag (Jordan et al., 2007),

which corresponds to z = 23.4 mag at the distance of NGC 4365 and all GC candidates

described in further analysis are brighter than this turnover magnitude.

The completeness of the S-Cam imaging was determined by keeping record of how

many of the HST/ACS detected objects were also detected by the Subaru/S-Cam imaging

at different magnitudes. The S-Cam imaging is found to be 50 per cent complete at


Figure 2.10: Estimate of the completeness of the S-Cam photometric sample by com-parison with HST/ACS photometry. The solid line shows the number of HST/ACS GCsin each magnitude bin, the dashed line shows the number of those objects that were alsodetected in the S-Cam imaging and the dotted line shows the percentage completeness ofthe S-Cam photometry as a function of magnitude.

i′ = 23.8 mag and 70 per cent complete at i = 23.6 mag as shown in Figure 2.10.

In the very central regions (radii < 0.325 arcmin) of NGC 4365 the HST/ACS detection

of GC candidates is inhibited by the high surface brightness of the galaxy. In Jordan

et al. (2007) the completeness of objects is tabulated as a function of object size, object

magnitude and surface brightness of the galaxy at the location of the GC candidate. For

this analysis it is only necessary to consider completeness as a function of radius. We

measured the average surface brightness of the galaxy in 0.1 arcmin annuli on the ACS

imaging. We compared that to the completeness fraction for an object of mean size = 0.039

arcsec (4.3 parsec) and z = 23.4 mag, which is representative of the GC candidate sample.

At distances of 0.2 and 0.325 arcmin from the galaxy centre the ACS imaging is 93.3 and

98.9 per cent complete.

2.4.4 Overview of GC selection

The selection of GC candidates from the Subaru/S-Cam photometry was done based

on object size, magnitude and locus in colour-colour space compared to GC candidates

obtained from HST/ACS photometry. Point-like objects were selected in each S-Cam

filter using a limit on the flux difference between two apertures (see Section 2.4.2). The


astrometry of the ACS GC catalogue was shifted to that of the S-Cam catalogue. The

positions of objects classed as point-like in all S-Cam filters were compared with the

positions of GC candidates obtained from the ACS photometry (see Section 2.4.1 for ACS

selection criteria; colours 0.7 < g − z < 1.6, sizes 0.1 pc < rh < 20 pc and magnitudes

26.2 ≤ z ≤ 19.0). The matched S-Cam point-like objects were used to determine the locus

of GCs on a r′−i′ vs. g′−r′ diagram. No contaminating objects could be visually identified

on inspection of the GC candidates brighter than i = 21 mag. Finally, estimates of the

contamination and completeness of the S-Cam catalogue were determined by comparison

with the ACS VCS catalogue (see Section 2.4.3).

The ACS catalogue determined here contains 2254 GC candidates with colours ranging

from 0.7 to 1.6 in g − z. The bright cutoff for GC candidates in the ACS catalogue is

z = 19 mag and the faintest GC candidate has z = 26.2 mag. The S-Cam catalogue

contains 5623 GC candidates (1327 of which are also found in the ACS catalogue) of

which 2615 are brighter than i′ = 23.6. The g′ − i′ colours of GC candidates in the S-

Cam catalogue range from 0.5 to 1.45. The bright cutoff for GC candidates in the S-Cam

catalogue is i′ = 19 mag and the faintest, reliable GC candidated has i′ = 24.5 mag.

3Wide-field imaging of NGC 4365’s globular cluster

system: The third subpopulation revisited

The most beautiful gift of nature is that it gives one pleasure to

look around and try to comprehend what we see.

—Prof. Albert Einstein

3.1 Introduction

Here we study NGC 4365, a giant elliptical (E3) galaxy (Ferrarese et al., 2006) on the far

edge of the Virgo Cluster, 23.1 Mpc away (Blakeslee et al., 2009) with MB = −21.3 mag

(Ferrarese et al., 2006). There is debate as to whether the apparent Kinematically Distinct

Core (KDC) of the galaxy is an actual kinematic property or the observed effect of triaxial

orbits (e.g. Davies et al., 2001; van den Bosch et al., 2008). NGC 4365 is also one of few

galaxies that rotate about the photometric minor rather than major axis (Davies et al.,

2001). Tal et al. (2009) claim evidence of a faint fan feature in the SW of the galaxy.

Forbes (1996) and Forbes et al. (1996) studied the GC system properties of NGC 4365

and other giant elliptical galaxies with KDCs. Bimodality was not suspected at the time in

most GC systems and they found no significant differences between NGC 4365 and other

giant elliptical galaxies with KDCs. Two independent groups working with HST/WFPC2

imaging, Larsen et al. (2001) and Kundu & Whitmore (2001), found that the GC system of

NGC 4365 was best fit by a unimodal distribution, whereas most of the elliptical galaxies

in their samples were best fit by bimodal distributions. Soon afterwards, Puzia et al.

(2002) analysed the GC system of NGC 4365, using the combination of HST/WFPC2

optical and VLT/ISAAC K-band photometry to break the age-metallicity degeneracy,

23

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24 Chapter 3. Wide-field imaging of NGC 4365’s globular cluster system

which is a problem inherent to photometric analysis of GCs. In their analysis they found

what appeared to be a slightly younger and very metal rich subpopulation in addition to

two old subpopulations. When Larsen et al. (2003) published Lick indices (obtained from

Keck/LRIS) of 14 GCs in NGC 4365 it still looked likely that the galaxy hosted some old

GCs and some very metal rich, young GCs that “conspire to produce the single broad

distribution observed in optical colors” (Larsen et al., 2003).

However, when Brodie et al. (2005) extended the sample of NGC 4365 GCs with Lick

indices by adding 19 new objects (also re-observing 3 objects from Larsen et al., 2003)

they found a uniform old age for all the GCs in the sample and suggested that the third

GC subpopulation in the system, also found at intermediate optical colours, was due to a

population with intermediate metallicity not young age. Hempel & Kissler-Patig (2004)

published “better age determinations” for the intermediate GC subpopulation by including

observations in another infrared filter and Kundu et al. (2005) approached the problem

with deeper HST observations in the H-band. Larsen et al. (2005) confirmed the presence

of intermediate optical colour GCs at small galactocentric radii (from an analysis of optical

HST/ACS photometry) and compared photometric age determinations with spectroscopic

ones to assess the accuracy of photometrically determined ages. This work cast some doubt

on the accuracy with which ages and metallicities can be measured using a combination

of infrared and optical photometry. Later, Hempel et al. (2007) published a photometric

comparison of elliptical galaxies in the centres of clusters and those in smaller groups,

claiming (from optical and near-infrared photometry) that many group elliptical galaxies

(including NGC 4365) host intermediate age GCs. Using g, z and K filter photometry,

Chies-Santos et al. (2011) showed that NGC 4365’s GCs all have similar ages (much like

the other large ellipticals in their sample) but that the distribution in the g-z direction

was significantly different to other large ellipticals.

While there is still debate on the nature (in age, colour and metallicity) of a third

GC subpopulation in NGC 4365, there is consensus that the GC system of NGC 4365

is different to other galaxies of similar luminosity. Most analyses of NGC 4365’s GC

system subpopulations to date have been done with tens or hundreds of GCs. We revisit

this issue in colour, using 6550 GC candidates from optical photometry over most of the

spatial extent of the galaxy, to analyse whether there is statistically significant indication

of three colour subpopulations in its GC system.

Section 3.2 describes the properties of the galaxy light and we proceed to the analysis

of the GC system properties in Section 3.3, first in terms of the properties of the total

GC system and then in terms of the subpopulations and their characteristics. We discuss

3.2. Analysis of the galaxy light 25

Filter µe n Re

(mag arcsec−2) (arcmin)g 21.87± 0.11 6.02± 0.21 2.40± 0.14r 21.46± 0.12 5.92± 0.23 2.06± 0.12i 21.20± 0.11 5.97± 0.24 2.10± 0.11

Table 3.1: Values for g, r and i Sersic profiles fitted to galaxy light.

results in Section 3.4 before concluding. Appendices A and B contain the statistical

analysis of NGC 4365’s GC subpopulations and GC surface density data respectively.

3.2 Analysis of the galaxy light

3.2.1 Parameters of the isophotal fits

We used the IRAF task ELLIPSE to model and subtract the galaxy light from the

Subaru/S-Cam photometry. From the model of the galaxy light we obtain the surface

brightness, ellipticity, position angle and higher order Fourier terms (S3, S4, C3, C4) in

g′, r′ and i′ filters for NGC 4365. Results are plotted in Figure 3.1 and briefly discussed

here. We have surface brightness information for all three filters between ∼ 0.1 and 5

arcmin. We fit Sersic profiles (Graham & Driver, 2005), i.e.

µ(R) = µe +2.5bnln10

[(R

Re

) 1n

− 1

](3.1)

to the three surface brightness profiles, where Re is the effective radius of the galaxy, µeis the surface brightness at that radius, n is the shape parameter of the Sersic profile and

bn = 1.9992n− 0.3271. The fitted values are tabulated in Table 3.1.

The quoted value for NGC 4365’s effective radius (Goudfrooij et al., 1994; Bender

et al., 1992) is 1.1 arcmin based on a de Vaucouleurs profile (equivalent to a Sersic profile

with the n parameter set to 4) fitted by Burstein et al. (1987). When a de Vaucouleurs

profile is fit to the data in this work the effective radius is found to be 1.32± 0.03 arcmin

but we find this to be a significantly poorer fit than a general Sersic profile. For all further

analysis we use our Re value of 2.1 ± 0.1 arcmin (14.1 ± 0.7 kpc), with an n value of

6.0 ± 0.2. This is close to the value of 1.6 ± 0.1 arcmin (10.9 ± 0.7 kpc) with n = 5.8 in

z that Chen et al. (2010) found and slightly smaller than the value of 3.07± 0.22 arcmin

(20.6± 1.5 kpc) with n = 7.1± 0.4 in B that Kormendy et al. (2009) found.

Results for the ellipticity, position angle and higher order Fourier terms in this work


Figure 3.1: Isophotal parameters for g′, r′ and i′ filters from Subaru/S-cam photometryof NGC 4365. From left to right and top to bottom the parameters plotted are: surfacebrightness of the galaxy light in g′ (crosses), r′ (stars) and i′ (plusses) filters; galaxy colour;ellipticity of the fitted isophotes in all three filters as labelled before; position angle of thefitted isophotes in all three filters as labelled before; higher order Fourier coefficients tothe sine (S3 and S4) and cosine (C3 and C4) terms. All parameters are plotted againstequivalent galactocentric radius, calculated as the geometric mean of the semi-major andsemi-minor axis lengths of the fitted ellipse. The fitted values become unstable beyond 3arcmin.

3.3. Analysis of the GC System 27

agree with the values Goudfrooij et al. (1994) found in the B, V and I filters and extend

2 arcmin further, to ∼ 1.5Re. The ellipticity measured in this work varies between 0.22 at

0.15 arcmin and 0.28 at 2.5 arcmin and is consistent with that seen by Goudfrooij et al.

(1994) interior to 1 arcmin. The position angle shows a small twist of ≤ 5◦beyond the

radial range explored by Goudfrooij et al. (1994). The S3 parameter (coefficient to the

sine term in the fit) shows an indication of increase at the edge of their radial range (1

arcmin) which is confirmed in this work (between 1 and 2 arcmin) and correspondingly the

S4 parameter increases between 1 and 2 arcmin. There is no significant difference in C3

and C4 parameters between Goudfrooij et al. (1994) and this work. The C4 disky/boxy

parameter remains at values between -0.01 and -0.02 out to 2 arcmin, indicating that NGC

4365 has boxy isophotal structure to large radii. The galaxy colour becomes bluer with

increasing galactocentric radius, changing by ∼ 0.1 mag from ∼ 0.1 to 2.5 arcmin for both

g − i and g − r colour indices. A similar trend, implying a negative metallicity gradient,

is seen in the Goudfrooij et al. (1994) B–I colour index.

3.3 Analysis of the GC System

3.3.1 Characterising the total GC System

Surface Density

By combining the wide field imaging from S-Cam with the spatially resolved imaging from

ACS and further restricting the GC candidate sample to objects brighter than the turnover

magnitude we can derive a very accurate radial surface density profile over a factor of 100

in radius. To derive surface density profiles, the number of GC candidates in each radial

bin were divided by the total area in that radial bin. The results for both the S-Cam and

ACS GC candidate samples are plotted on Figure 3.2.

We expect very little contamination in the density profile, with more than 90 per

cent completeness in this magnitude range (see Section 2.4.3). A Sersic profile plus a

background parameter, i.e.

P (R) = Pe exp

(−bn

[(R

Re

) 1n

− 1

])+ bg (3.2)

where

bn = 1.9992n− 0.3271 (3.3)

is fitted to the combined S-Cam and ACS radial surface density of GC candidates and


Figure 3.2: Surface density of GC candidates brighter than the turnover magnitude plot-ted against galactocentric radius. The S-Cam points (solid points) were calculated usingobjects brighter than i′ = 23.6 and the ACS points (hollow diamonds) were calculatedusing objects brighter than z = 23.4. The innermost two ACS values were corrected forincompleteness using the values discussed in the text. No other normalisation correctionhas been made to either ACS or S-Cam radial surface density and the remarkable agree-ment in the region of overlap (between 1 and 3 arcmin) is an indication that both samplesare almost perfectly complete to the turnover magnitude and contain very little contam-ination. The line plotted is a fitted Sersic profile with a constant background value. Seetext for further details.


Pe n Re bg(arcmin−2) (arcmin) (arcmin−2)3.9± 1.3 2.68± 0.41 6.1± 1.2 0.89± 0.13

Table 3.2: Fitted values of the Sersic fit to the GC surface density.

shown in Figure 3.2. Re is the effective radius of the GC system, Pe is the density at the

effective radius and n is the shape parameter of the Sersic profile. The fitted parameters

are recorded in Table 3.2. The value for contamination expected from the analysis in

Section 2.4.3 (1.23±0.19 arcmin−2) agrees within 2σ with the value from the Sersic profile

fit (0.89± 0.13 arcmin−2).

We are not able to define the edge of NGC 4365’s GC system with certainty as the

profile shown in Figure 3.2 is still decreasing at ∼ 20 arcmin (134 kpc), which is the very

edge of the spatial coverage available to us with S-Cam imaging. In order to confirm the

edge of the GC system even larger field of view imaging would be required. We can make

an estimate of the total number of GCs associated with NGC 4365 by simply doubling the

number of GC candidates brighter than the turnover magnitude (i′ = 23.6 and z = 23.4).

This assumes that there is no inherent asymmetry in the GC luminosity function, the GC

system of NGC 4365 contains few GCs beyond 20 arcmin and that the sample is complete

and free of contamination. We determine the total number of GCs in NGC 4365’s GC

system to be 6450±110. This is likely to be a lower limit and doubles the expected number

of GCs in NGC 4365 from the value of 3246± 598 calculated by Peng et al. (2008). They

use the narrow field of view of one HST/ACS and three HST/WFPC2 pointings to derive

an extrapolated GC surface density profile that is not publicly available. We suggest that

the surface density profile that Peng et al. (2008) fit to the GC candidates underestimated

the radial extent of NGC 4365’s GC system.

These total GC numbers convert to specific frequency (Harris & van den Bergh, 1981)

values of SN = 3.86± 0.71 (Peng et al., 2008) and SN = 7.75± 0.13 determined from this

work for MV = −22.31. Several other large elliptical galaxies with similar MV have specific

frequency values comparable to the SN measured here. NGC 1407 has SN = 7.98± 0.87,

NGC 1399 has SN = 6.72 ± 0.81 (Spitler et al., 2008) and Peng et al. (2008) find SN =

5.2 ± 1.4 for M84. Fitting a power law to the radial density beyond the Sersic effective

radius we find a slope (i.e. power-law exponent) of −1.21±0.03 to allow comparisons with

analysis of other galaxies in the literature.

By binning the GC candidate positions into R.A. and Dec. defined squares we derive

a two-dimensional density distribution for the GC candidates. This is shown in Figure 3.3


Figure 3.3: Surface density of GC candidates in two dimensions from S-Cam data. Thecolour ranges from red to blue indicating decreasing GC surface density. Ellipses showingthe ellipticity and position angle of the GC system are plotted with dotted black lines andgalaxy light isophotes are plotted for comparison in solid black lines. The GC system ismuch more elongated than the starlight.


Figure 3.4: Histogram of the azimuthal distribution of GC candidates. Poisson errorsare shown on the histogram and the dotted line with best fit ellipticity and position angleis overplotted. See text for fitting and parameter details.

along with selected galaxy surface brightness isophotes from the i′ filter as determined by

the IRAF task ELLIPSE. McLaughlin et al. (1994) use

σ(R, θ) = kR−α[cos2(θ − PA) + (1− e)−2 sin2(θ − PA)]−α/2 + bg (3.4)

to fit for the position angle (PA) and ellipticity (e) of the GC system of M87. They

fit to the density of GCs as a function of azimuthal distribution and we follow the same

procedure. We use the previously determined power law exponent (α) and the fitted

background value (bg), allowing the normalisation constant (k) as well as the position

angle and ellipticity to vary. We find the position angle of the GC system to be 39.7±2.2◦

and the ellipticity to be 0.40 ± 0.02, see Figure 3.4. The GC system of NGC 4365 has

a similar position angle to the galaxy light (∼ 42◦) but is clearly much more elliptical

than the starlight of NGC 4365 (which has an ellipticity of ∼ 0.25). We see qualitative

agreement with this analysis in Figure 3.3.

Colour Distribution

Before performing a quantitative analysis of the surface density characteristics of GC

subpopulations in NGC 4365 we can make a qualitative assessment of colour-radius sub-

structure by examining the surface density of GCs in colour-radius space. This is shown in


Figure 3.5: GC density with colour and galactocentric radius for the ACS data. Thecolour scale indicates object number per square arcmin per colour bin ranging from blackat low density to red at high density. The plot is constrained to objects with radii of 0.02to 5.8 arcmin. (Top) Measured GC density with colour and radius is plotted. (Centre)The model of red and blue GC density calculated using the Gaussian positions and widthsfrom the KMM results for the two subpopulations at large radii (r > 3.5 arcmin). Theproportion of GCs in each Gaussian distribution is taken from the KMM results for thewhole sample and the Sersic radial density profile used is calculated in Section 3.3.1. Thiswas scaled to have a similar object density to the data at large radii. (Bottom) Themodel in the central panel has been subtracted from the density distribution in the toppanel to show an approximate distribution of GCs that are not well modelled by a bimodalcolour distribution of GCs. This distribution is not a robust measure but it does showindications of significant numbers of intermediate colour GCs (g − z ∼ 1.1), particularlyat small radii (. 2 arcmin).


Figure 3.6: GC density with colour and galactocentric radius for the S-Cam data. Thecolour scale indicates object number per square arcmin per colour bin ranging from blackat low density to red at high density. The plot is constrained to objects with radii of 0.3 to12 arcmin. There is indication of intermediate colour (g′ − i′ ∼ 1.05) GCs at small radii,similar to the ACS data, but it is not clear whether this intermediate colour subpopulationis evident at larger radii.

Figures 3.5 and 3.6. At galactocentric radii larger than 2 arcmin in the top panel of Figure

3.5 the blue and red GC subpopulations of NGC 4365 are visible as distinct sequences at

g−z ∼ 0.9 and ∼ 1.3 respectively. At radii smaller than 2 arcmin and possibly between 2.5

and 3.5 arcmin there is evidence of a third subpopulation of GCs intermediate in colour.

Note the clear overdensity of GCs candidates at intermediate to red colours and within 1

arcmin in both Figures 3.5 and 3.6. These data confirm a similar observation by Larsen

et al. (2005) where evidence of intermediate colour GCs were found at radii smaller than

1.25 arcmin. We are able to extend the analysis of the GC colour substructure with radius

to 5.5 arcmin with HST/ACS imaging (see Figure 3.5) and to 11.5 arcmin with S-Cam

imaging (see Figure 3.6). Beyond 3 arcmin in Figure 3.6 the colour distribution seems

to be dominated by 2 colour modes but there is some indication of GCs at intermediate

colours.

Recently, observers have found gradients in the peak colour of the GC subpopulations

with galactocentric radius (see Harris, 2009a,b; Liu et al., 2011; Forbes et al., 2011). Both

blue and red subpopulations’ peak colours shift to bluer colours at larger radii. Forbes

et al. (2011) find the most extreme gradient to date in the galaxy NGC 1407. If the red

subpopulation of NGC 4365 were similar to that of NGC 1407 then the peak colour for the

red subpopulation would change from g′ − i′ ∼ 1.19 at the galaxy centre to g′ − i′ ∼ 1.06

at 10 arcmin, and the red subpopulation would have intermediate colours at large radii.

In the bottom panel of Figure 3.5 and in Figure 3.6 an overdensity of GC candidates

with intermediate colours is visible at very small radii that cannot be accounted for by a


Figure 3.7: Rolling peak colour values found by the KMM code plotted against galacto-centric radius. The bimodal case is plotted in the top panel and the trimodal case in thebottom panel. S-Cam (g′ − i′) rolling peak values are plotted for blue (dashed), green(dash-dotted) and red (dotted) modes in the multimodal fits. Galaxy g′ − i′ colours areplotted as black dots with errorbars and the solid black lines show best fit gradients incolour-log radius space.

gradient in the peak colour of the red subpopulation. We note that if this intermediate

colour overdensity at small galactocentric radii is a distinct subpopulation and there is

a gradient in the red subpopulation of NGC 4365 then statistical tests for bimodality or

trimodality based on colour distribution alone might not be able to distinguish between

these two subpopulations conclusively.

3.3.2 Radial colour gradients

Several groups have recently found shallow but significant gradients in the mean colour

of GC subpopulations (see Harris, 2009a,b; Liu et al., 2011; Forbes et al., 2011). The

radial extent of the Subaru/S-Cam imaging makes this an excellent data set to deter-

mine whether there are gradients present in the GC subpopulations of NGC 4365. The

presence of an intermediate colour subpopulation will influence the gradient determined

and therefore we consider the case of a bimodal and a trimodal distribution separately.

The heteroscedastic KMM algorithm was run on a rolling sample of GC candidates and

the peak value determined for both bimodal and trimodal instances for the S-Cam data

set. The rolling sample consisted of 360 GC candidates input to KMM at each step and

between each step the 120 candidates with lowest galactocentric radius were replaced with


Subpopulation Bimodal Case Trimodal Case(dex per arcmin) (dex per arcmin)

Blue −0.0037± 0.0002 −0.004± 0.001Green - 0.001± 0.012Red −0.0047± 0.0005 −0.009± 0.002

Table 3.3: GC colour gradients for the linear fit to colour with radius in arcmin (Equation3.5) for S-Cam data.

Subpopulation Bimodal Case Trimodal Case(dex per dex) (dex per dex)

Blue −0.055± 0.004 −0.04± 0.01Green - −0.01± 0.10Red −0.062± 0.008 −0.07± 0.02

Table 3.4: GC colour gradients for the logarithmic fit to colour with normalised radius(Equation 3.6) for S-Cam data.

the 120 candidates closest to, but at least as far as, the furthest candidate in the previous

sample. A linear relation was fitted to the S-Cam peak colours, to 14.5 arcmin in the

bimodal case and 7 arcmin in the trimodal case, i.e.

g′ − i′ = a+ bR(arcmin) (3.5)

Results are tabulated in Table 3.3. The linear blue gradient values are consistent within

errors but the linear red gradient is significantly steeper in the trimodal case. The green

gradient measured in the trimodal case is consistent with zero.

We also fit a line to the logarithm of the radius normalised by Re using Re = 2.1

arcmin, i.e.

g′ − i′ = c+ d log(R/Re) (3.6)

Results are tabulated in Table 3.4. Here both blue and red gradients are consistent within

errors for the bimodal and trimodal cases. The green subpopulation does not have a

measurable gradient in this case either. Liu et al. (2011) measure colour gradients for

NGC 4365 GCs using the central ACS pointing in the bimodal case, finding a blue slope

of −0.056 ± 0.024 and a red slope of −0.033 ± 0.021 dex per dex. The radial range of

their measurements do not overlap with ours and we see that the radial colour gradient of

the blue subpopulation remains constant when moving to larger galactocentric radii while

the radial colour gradient of the red subpopulation steepens significantly with increas-

ing galactocentric radius. We use the Lee, Park & Hwang (2010) empirical relationship


Subpopulation Bimodal Case Trimodal Case(dex per dex) (dex per dex)

Blue −0.19± 0.01 −0.13± 0.03Red −0.22± 0.03 −0.26± 0.06

Table 3.5: Metallicity gradients for the logarithmic fit to colour with normalised radius(Equations 3.6 & 3.7) for S-Cam data.

between GC colour and metallicity to give a metallicity gradient, i.e.

∆[Fe/H] = 3.48∆(g′ − i′) (3.7)

Results are shown in Table 3.5. The gradient of the mean metallicity with galacto-

centric radius is shallower in the trimodal case for the blue subpopulation but for the red

subpopulation the gradients in the bimodal and trimodal cases are consistent. We note

that the rolling peak colour found by KMM is only consistent with the galaxy colour in

the trimodal case (see Figure 3.7).

We conclude that the NGC 4365 GC subpopulations show a shallow but significant

colour gradient with galactocentric radius, regardless of whether the GC system is in-

terpreted as being bimodal or trimodal. Forbes et al. (2011) tabulate metallicity-radius

gradients in units of dex per dex for several galaxies on which this measurement has been

done. The gradients measured for NGC 4365 are steeper than the mean, but not the

steepest measured. Results vary from −0.10 to −0.38 for the blue subpopulation and

from −0.10 to −0.43 for the red subpopulation in Forbes et al. (2011) Table 1. These

gradients explain why statistical analyses of the colour distribution (collapsed in radius)

of NGC 4365’s GC system give inconclusive results.

3.3.3 Colour-magnitude trends

In the determination of the colour-magnitude trends we only investigate the blue sub-

population of the GC system, we search for a ‘blue tilt’ (Strader et al., 2006). We do

this in order to decide whether the blue tilt could be the cause of the intermediate colour

overdensity seen in Figures 2.5, 3.5 and 3.6. The bimodal KMM mean colour values at

various magnitudes were determined by dividing the GC sample into magnitude subsets

with equal numbers. The mean z magnitude of GC candidates in each subset is plotted

against the blue heteroscedastic Gaussian mean value found by KMM in Figure 3.8. We

did this analysis for the central HST/ACS pointing as well as for the full set of eight

HST/ACS pointings and found that both cases show a significant gradient but that the


Figure 3.8: The colour-magnitude relation for blue GC candidates found in theHST/ACS photometry. The left panel shows GC candidates found in the central ACSpointing and the right panel shows candidates found in all eight ACS pointings. TheKMM mean value for the blue GCs is overplotted with blue points. The one sigma rangefor the linear fit to those values is overplotted with a blue shaded region on both panels.On the left panel the gradient the slope found by Mieske et al. (2006) for galaxies between−21.7 < MB < −21 is overplotted with a dashed line.


slope is steeper in the central region (−0.0477 ± 0.0018) than for all pointings combined

(−0.024±0.013). Due to the smaller number of GCs when restricting to the central point-

ing, one magnitude bin (z = 22.75) is shifted to redder colours because there are a lack

of GCs bluer than g − z = 0.9 there. This does not significanlty change the fit. Mieske

et al. (2006) use the central ACS pointing and combine GCs for all galaxies in the range

−21.7 < MB < −21 to determine a slope of −0.037±0.004 using the same KMM method.

In Figure 3.8 we see that this lies very close to our fitted line. They also find a steepening

slope when GCs closer to the galaxy centre are used.

In Figure 2.5, indication of a clump of intermediate colour/green objects is seen around

g − z ∼ 1.1 and 22 > z > 20 and in the bottom panel of Figure 3.5 a clear overdensity is

seen inside 2 arcmin and around g− z ∼ 1.1. The colour magnitude slope for the blue GC

candidates is not steep enough to cause either of these overdensities.

3.3.4 Quantifying the GC subpopulations

We compare NGC 4365’s GC colour distribution with that of other Virgo cluster galaxies,

using the ACS Virgo Cluster Survey (VCS) photometry (Peng et al., 2006). They fit a

homoscedastic bimodal distribution using Kaye’s Mixture Model (KMM) algorithm and

obtained mean g − z colour values of 0.98 and 1.36 for the blue and red subpopulations

of NGC 4365 respectively, as well as a width of 0.15 for both subpopulations. They also

fit linear relations between mean g − z colour and absolute galaxy magnitude, MB, for

both GC subpopulations, assuming all Virgo Cluster galaxies have the same distance. The

relations predict mean g − z colour values of 0.98± 0.06 and 1.40± 0.08 for the blue and

red subpopulations when the conventional distance for NGC 4365 (23.1 Mpc) is used.

They concluded that aside from having a large number of red GCs, NGC 4365 has an

unremarkable colour distribution compared to their sample (Peng et al., 2006).

Fitting a bimodal homoscedastic distribution to NGC 4365’s GCs, with KMM, using

all eight available ACS pointings we find similar values to the ones found by ACS VCS (i.e.

g− z = 0.94 and 1.30 for the blue and red peak colours and a width of 0.12). However, on

visual inspection the fit to the GC distribution is skewed to redder colours when compared

to the actual colour distribution. We also fit both bimodal and trimodal heteroscedastic

distributions to the GC colour distributions, obtaining mean blue and red values of g−z =

0.89 and 1.25 in the bimodal case, and blue, green and red values of 0.88, 1.12 and 1.34

in the trimodal case. In neither case does the mean blue colour lie close to the g − z vs.

MB relation (values are 1.4σ and 1.6σ away from the relation) but only in the trimodal

case does the red mean colour lie close to the Peng et al. (2006) g − z v.s. MB relation,


ACS (g − z) S-Cam (g′ − i′)peak width peak width

Blue 0.89 0.07 0.80 0.07Red 1.32 0.12 1.13 0.10

Table 3.6: Gaussian values for the blue and red GC distributions as determined from anEpanechnikov smoothing kernel of the colour distribution.

where the value is 0.7σ away from the relation instead of 1.8σ away. This is a motivation

to describe the GC system of NGC 4365 in terms of three rather than two subpopulations

(see Appendix A for further details).

From this point forward we assume that the GC system of NGC 4365 contains three

subpopulations, a blue, green (intermediate colour) and red subpopulation. There are

radial gradients in colour and a blue tilt but in neither case are they enough to explain the

strong overdensity seen at intermediate colours in both Figures 3.5 and 3.6. In addition,

there is better agreement with the colour - galaxy luminosity relations of Peng et al. (2006)

to the other Virgo cluster galaxies and a near perfect match between galaxy light and mean

red subpopulation colours if we assume that NGC 4365 has three subpopulations.

In the trimodal case, the green GCs would likely ‘contaminate’ the blue and red sub-

populations to all except the bluest and reddest colours. A simple split based on colour

would leave a high percentage of green GCs in both the blue and red subpopulations and

visa versa. Therefore, we formulate a probability that a GC belongs to either a blue, green

or red subpopulation based on its colour and galactocentric radius. Note that red (metal

rich) GCs are generally found to be more centrally concentrated than blue (metal poor)

GCs (Brodie & Strader, 2006) thus GC galactocentric radius is an important factor in the

probability calculation. We assume that each of the three subpopulations are fairly well

described by a Gaussian in colour (without skewness or kurtosis) and that the peak and

width of the Gaussian is independent of galactocentric radius (i.e. no radial colour gra-

dient) but make no further assumptions about the radial distribution of any of the three

subpopulations. Given evidence of radial colour gradients we note that this assumption is

strictly inaccurate. We make this assumption because colour gradients are shallow com-

pared to the width of the GC colour distributions and the calculation of gradient values

contain other significant assumptions about the radial distribution of the subpopulations

that we wish to avoid. The following paragraphs describe how we assign a probability that

a GC is blue, green or red (henceforth blue-green-red probability).

The peak of the blue Gaussian distribution was determined from the bluest peak of an

Epanechnikov kernel smoothing of the colour distribution (Silverman, 1986) and the width


Figure 3.9: Colour distribution of GC candidates at all radii for S-Cam (top) andACS (bottom) brighter than the turnover magnitude (z = 23.4 and i′ = 23.6). TheEpanechnikov kernel smoothing of the colour distribution is plotted with a dotted line,the smoothing width used is the average colour error (0.04 for both ACS and S-Cam). Atleast three peaks can be seen.


Figure 3.10: The normalised distribution of ACS GCs with colour in the seven radialbins (shown in arcminutes on each plot) and at all radii (lower right panel). Histogramheights are shown as black circles with errorbars representing Poissonian errors. FittedGaussian distributions are plotted in blue and red for the respective subpopulations andthe remaining GCs (once blue and red distributions are subtracted) are plotted as greensquares. The peak and width parameters for the Gaussian distributions fitted in each caseare constant: µb = 0.89, σb = 0.07, µr = 1.32 and σr = 0.12, with the normalisationallowed to vary.


of the blue Gaussian distribution was determined from the colour at which 68 per cent of

the GCs bluewards of the peak were included in the distribution (assuming that green GCs

would not be present in significant numbers at such blue colours). See Figure 3.9 for the

S-Cam colour distribution and kernel smoothing. The peak position and width of the red

distribution were calculated similarly and all values are shown in Table 3.6. As seen in the

bottom right panels of Figures 3.10 and 3.11, where GCs at all radii are included for each

sample, the Gaussian distributions for blue and red subpopulations determined in this

way are a reasonable fit to the extreme blue and red ends of the GC colour distribution.

To assess the number of blue, green and red GCs as a function of colour and radius,

the GCs in each sample (ACS or S-Cam) were split into radial bins of roughly equal

object number and for each radial bin blue and red Gaussian distributions were fitted to

the normalised colour histogram. Peak and width values were held constant across radial

bins, as determined from the whole sample (see Table 3.6), and the normalisations of

the distributions were fitted separately for each radial bin. This was a χ2 minimization

fit. The blue and red numbers were calculated by adding the GCs in the extreme colour

regions (where no significant green ‘contamination’ is expected) to the percentage of the

total objects expected to belong to either the blue or red subpopulations (obtained by

integrating the blue and red Gaussian distributions over intermediate colours). The green

GC numbers were defined by subtracting both the blue and red subpopulation numbers

from the total number in the distribution. These values were used to determine a radial

surface density profile for each subpopulation (discussed further in Section 3.3.5), which

is used to calculate the probability that a GC at any radius or colour belongs to the blue,

green or red subpopulations.

The process used to determine the blue-green-red probability of a GC starts by de-

termining the relative number of GCs in each subpopulation at the galactocentric radius

of the GC. This is done by comparing the values of the radial surface density profile for

each subpopulation. The relative numbers of GCs in each population are used to scale

the normalisations of the three Gaussian distributions and comparing the relative values

of all three Gaussian distributions at the colour of the GC we calculate the probability of

an object being blue, green, or red.

3.3.5 Characterising the GC system subpopulations

Surface density

As described in Section 3.3.4 we counted the blue, green and red GCs in each radial bin

assuming that the red and blue GC colour distributions are well fit by Gaussians. In


Figure 3.11: The normalised distribution of S-Cam GCs with colour in seven radial bins(shown in arcminutes on each subplot) and at all radii (lower right panel). Histogramheights are shown as black circles with errorbars representing Poissonian errors. FittedGaussian distributions are plotted in blue and red for the respective subpopulations andthe remaining GCs (once blue and red distributions are subtracted) are plotted as greensquares. The peak and width parameters for the Gaussian distributions fitted in each caseare constant: µb = 0.80, σb = 0.07, µr = 1.13 and σr = 0.10, with the normalisationallowed to vary.


Figure 3.12: GC radial surface density for blue (squares), green (triangles) and red(circles) subpopulations incorporating ACS (unfilled points) and S-Cam (filled points)GCs. The surface density for both the blue and red GC subpopulations are well fit by aSersic profile added to a background term (shown as blue and red solid lines). The surfacedensity for the green subpopulation is best fit by a power law plus a background value(shown as a green solid line). The red dashed line is a scaled version of the Sersic profile fitto the red subpopulation that is a reasonable fit to all except the inner two green surfacedensity points. See text for further details.


Pe n Re bg(arcmin−2) (arcmin) (arcmin−2)

Blue 1.67± 0.29 1.36± 0.19 7.30± 0.68 0.25Green 1.71± 0.17 2.02 3.17 0.21± 0.03Red 4.91± 0.59 2.02± 0.25 3.17± 0.19 0.50

Table 3.7: Sersic profile fits to the radial surface density of the blue, green and red GCsubpopulations. The green subpopulation n and Re values have been fixed to the redsubpopulation values.

Figure 3.12 the radial surface density data for all three subpopulations are shown with

the best fit surface density model in each case (data is tabulated in Appendix B). The

red and blue subpopulation distributions are both well fit by a Sersic profile added to a

background term. The values we found are recorded in Table 3.7. The background terms

were estimated from the outermost radial surface density value. The effective radius,

or half number radius, of the blue subpopulation (7.30 arcmin) is more than twice as

large as the effective radius of the red subpopulation (3.17 arcmin) and the Sersic n

values are consistent within combined errors. The green subpopulation can be fit by the

red subpopulation Sersic profile if the two innermost density points are omitted (points

interior to 1.5 arcmin) and we fit for Pe and the bg (see Table 3.7 for the fitted values).

We also fit a two parameter power law plus a background term

ρ(R) = ρ0Rα + bg (3.8)

to the entire radial range of the green subpopulation, finding α = −1.71 ± 0.13, ρ0 =

12.3± 1.3 arcmin−2 and bg = 0.10± 0.05 arcmin−2. Comparing the slope of a power law

fit to the blue (−1.13±0.06) and red (−1.60±0.04) distributions over a radial range from

0.5 to 11 arcmin (4 to 70 kpc) we can quantitatively verify that the green and red GC

subpopulations are more centrally concentrated than the blue GC subpopulation and that

the slope of the green and red subpopulations are consistent with each other.

In Figure 3.13 the percentage of the total GC candidate number that is attributed

to each subpopulation is plotted as a function of galactocentric radius. The data points

have been background subtracted and the curves derived from the profile fits in Figure

3.12 are plotted without the addition of background terms. In the case of the green

subpopulation the curve shown is a combination of the power law fit in the inner parts

(R < 2.25 arcmin) and the Sersic profile beyond that. We do this to adequately display

the behaviour of the intermediate colour surface density both in the inner (power law

behaviour) and outer (Sersic behaviour) parts, noting that we plot lines to guide the eye


Figure 3.13: The percentage contribution of each subpopulation to the total numberof GC candidates plotted against galactocentric radius. Curves were calculated using thefitted surface density profiles in Figure 3.12. Both the data points and plotted profileshave had the respective background values subtracted. Beyond 12 arcmin the total pop-ulation numbers are low and the measurement of the percentage contribution of any onesubpopulation to the total number becomes unstable. We see that the red subpopulationdominates at small galactocentric radii and the blue dominates at large galactocentricradii while the green subpopulation is only significant at very small galactocentric radii.


Figure 3.14: The half light radius (rh) of HST/ACS GC candidates against galactocen-tric radius for objects brighter than z = 23.4. The high probability blue, green and redGC subpopulation candidates are plotted with blue squares, green triangles and red circlesrespectively. There is a trend of increasing GC candidate size with increasing galactocen-tric radius for all GCs regardless of subpopulation colour. A power law fitted to the wholesample of GC candidates brighter than z = 23.4 has a slope of 0.49 ± 0.04 dex per dex.Overplotted with blue dashed, green dash-dotted and red dotted lines are the power lawfits for size normalisation (with slope set by whole GC sample) of each high probabilitysubpopulation sample.

and not for analysis. In agreement with previous work (see review by Brodie & Strader,

2006) the red subpopulation dominates the GC system in the inner parts and the blue

subpopulation dominates in the outer parts. We also see that the green subpopulation

only contributes significantly to the GC system at very small galactocentric radii (r . 2

arcmin). This is in agreement with the qualitative analysis of the colour-galactocentric

radius properties of the GC system earlier in this work (see Section 3.3.1) and with Larsen

et al. (2005).

GC half light sizes

Here we measure subpopulation sizes (median half light radii) as well as trends in candidate

size with galactocentric radius from the ACS photometry. As described in Section 3.3.4

each GC candidate is assigned a probability of belonging to each subpopulation. For

example a candidate 1.28 arcmin from the galaxy centre with g−i = 0.98 has probabilities:


pblue = 18 per cent, pgreen = 79 per cent and pred = 3 per cent. Defining the blue and red

GCs with a colour probability larger than 95 per cent and green GCs with a probability

larger than 80 per cent there are 315, 426 and 491 GC candidates in each of the blue, green

and red high probability subsamples. There are no green GC candidates with a probability

larger than 95 per cent so we compare medium probability green candidates (80-95 per

cent) with high probability blue and red candidates. The aforementioned example is not

defined as part of either blue, green or red high probability subsamples.

We determine the median half light radius of the GCs;

rh,blue = 0.037±0.0020.002 arcsec

rh,green = 0.027±0.0010.001 arcsec

rh,red = 0.025±0.0010.001 arcsec

These sizes were measured on GCs brighter than z = 23.4 (109, 223 and 269 blue, green

and red high probability subsample GCs) using ISHAPE (Larsen, 1999) where the method

is described in Strader et al. (2006). The median sizes correspond to 4.1±0.30.2, 3.0±0.2

0.1 and

2.8±0.10.1 parsec respectively. Blue GCs have a significantly larger median size than either

green or red GCs. However, the sizes of the green GCs are only slightly larger than the

red GCs. This is possibly due to distinct characteristic sizes for each subpopulation but

could also be explained by a continuous trend in GC size with GC colour (e.g. Jordan,

2004).

As well as a possible trend of GC size with colour we also see a trend of GC size

with galactocentric radius. In Figure 3.14 a trend of increasing GC candidate size with

increasing galactocentric radius is visible. Fitting a two parameter power law to the GC

candidates brighter than z = 23.4 we find

rh(pc) = [1.00± 0.04]R(kpc)[0.49±0.04]

All three subpopulations show a clear size increase with distance from the galaxy centre

with a power law slope of 0.49 ± 0.04 dex per dex. This measurement extends to 4.5Re,

one of only four similar measurements extending beyond ∼ 2Re. Harris (2009a) measured

a slope of 0.11 on a composite sample of six massive galaxies, Gomez & Woodley (2007)

measured a slope of 0.05± 0.05 for metal poor GCs and 0.26± 0.06 for metal rich GCs in

NGC 5128 and Spitler et al. (2006b) measured a slope of 0.19 ± 0.03 in NGC 4594. The

slope we measure is steeper than any of the previous values and much closer to the value

measured for our own Galaxy (0.36 ± 0.07 for metal rich GCs, see Gomez & Woodley


Figure 3.15: The normalised mass function for GC candidates brighter than z = 23.4from the ACS catalogue. All GCs (black) as well as the high probability subpopulationcandidates (plotted in blue dashed, green dash-dotted and red dotted lines respectively).Each subpopulation has a significantly different mass distribution to the others.

2007). We conclude that there is more variability, between galaxies, in the relationship

of GC size with galactocentric radius than previously found. Shown in Figure 3.14 are

the GCs in all three high probability subpopulation samples and power law fits. We set

the slope of the fits to 0.49 and found normalisations of 1.096± 0.042, 0.994± 0.059 and

0.944 ± 0.044 for the blue, green and red samples respectively. This results agrees with

analysis of GC subpopulation median sizes.

Mass function

We compare the mass function of the high probability subpopulation GC candidates (as

defined in Section 3.3.5). The mass for each GC candidate was derived using the colour

dependent values for the z filter mass-to-light (M/Lz) ratio in Table 5 of Jordan et al.

(2007). They used PEGASE 2.0 with a modified Salpeter initial mass function from

Kennicutt (1983) and an age of 13 Gyr for all GCs. We used magnitudes brighter than

z = 23.4 to calculate the mass functions (plotted in Figure 3.15). We ran the Kolmogorov-

Smirnov (KS) test on each pair with a common mass range of 2× 105M� < M < 107M�.

The KS test gives a probability of 98.3 per cent that the blue and red subpopulations

are drawn from different distributions and gives a probability of more than 99.9 (99.4)

per cent that the green subpopulation is drawn from a different distribution than the


blue (red) subpopulation. The discrepancy between the three subpopulations is greatest

around 1× 106M� in the cumulative mass distribution the KS test uses for its analysis.

Analysis of the mass function of GC subpopulations indicates that the green subpopu-

lation is distinct from both blue and red subpopulations, containing a significantly larger

percentage of objects between 106M� and 107M� than either blue or red subpopulations,

as can be clearly seen in Figure 3.15. This overdensity of green subpopulation objects can

also be seen in Figure 2.5 around g − z ∼ 1.1 and 22 > z > 19. It is possible that UCDs

or dE nuclei contaminate the bright, mid-colour range (Strader et al., 2006) to produce

this feature.

Azimuthal properties

In Figure 3.16 we show the azimuthal distribution of high probability colour subpopulation

GC candidates (as defined in Section 3.3.5) for S-Cam and ACS. We restrict the samples

to radii where we have complete azimuthal coverage so as not to bias the samples in any

direction. The position angle data is folded at 180◦ to improve number statistics. The

bottom panel of Figure 3.16 shows that there is no position angle signal within errors for

any of the three subpopulations in the ACS sample. The S-Cam azimuthal distribution in

the top panel does show some structure; the high probability blue GC candidates are more

likely to have position angles between 0◦ and 90◦, which is consistent with the blue GC

subpopulation being elongated along the major axis of NGC 4365 (the galaxy has a position

angle of ∼ 42◦, see Section 3.2). The red distribution shows an almost flat azimuthal

distribution (indicating an almost circular GC system) and the green distribution shows

a very shallow sinusoidal distribution with a peak between 0◦ and 90◦. It is clear that

the blue GC system is very elongated along the position angle of the galaxy and likely

that both green and red subpopulations also have a similar position angle to the galaxy

starlight. We obtain estimates of the ellipticity of the blue, green and red subpopulations

by fixing the position angle (to 42◦) as well as the power law exponent and background

value as described in Section 3.3.1, Equation 3.4, following the same fitting procedure.

We find that the estimated ellipticity for the blue and green subpopulations are the same

within errors and also consistent with the ellipticity measured for the whole GC sample

in Section 3.3.1. The estimated ellipticity of the red subpopulation is much smaller than

the blue and green values. It is consistent with zero and also the ellipticity measured for

the galaxy light (see Table 3.8).

We also split each subpopulation into an inner and outer radial bin with equal numbers

in each and refit for ellipticity but did not detect radial variation in ellipticity for any of


Figure 3.16: Histograms of the azimuthal distributions of high probability blue (dashed),green (dash-dotted) and red (dotted) GC subpopulations. The top panel shows the posi-tion angles for the S-Cam GC candidates out to 11 arcmin from the galaxy centre and thebottom panel shows the ACS GC candidates out to 3.38 arcmin from the galaxy centre.The position angle of the galaxy is ∼ 42◦ and both blue and green subpopulations showGC overdensity between 0◦ and 90◦ in the S-Cam catalogue.


Galaxy Light Blue Green Red0.25± 0.03 0.44± 0.08 0.30± 0.08 0.03± 0.08

Table 3.8: Estimated ellipticity for blue, green and red GC subpopulations. The positionangle of each GC subpopulation is fixed to be the same as that of the galaxy light (42◦).

Figure 3.17: The radial surface brightness profile of the galaxy light from the S-Cami′ filter compared with the surface density profile of the GC subpopulations. The GCsubpopulations are plotted as blue squares, green triangles and red circles. The greypoints show the surface brightness measurements and the black line shows the Sersic profilefit to the galaxy light (see Section 3.2). All parameters are plotted against equivalentgalactocentric radius, calculated as the geometric mean of the semi-major and semi-minorradii. The green and red GC surface density profiles are similar to the galaxy light surfacebrightness profile.

the three subpopulations.

The agreement in ellipticity between the galaxy light and the red GC subpopulation

is consistent with literature findings that red GC subpopulations generally follow the

properties of galaxy field stars more closely than blue GC subpopulations. The blue

GC subpopulation dominates in the outer regions of the galaxy and also dominates the

measurement of the ellipticity of the GC system. It can be seen qualitatively in Figure 3.3

and quantitatively from the results in Table 3.8 that the blue subpopulation is significantly

more elliptical than the galaxy field stars.

3.4. Discussion 53

3.3.6 Comparison with galaxy surface brightness

The galaxy surface brightness profile from the i′ filter photometry is compared with the GC

surface density profiles in Figure 3.17. We compare the galaxy surface brightness profile

with 2.5 times the logarithm of the GC surface density profile and arbitrarily scaled for

ease of viewing, i.e.

2.5log[P (R)− bg] (3.9)

where P (R) is the GC radial surface density profile and bg is the determined background

value. Visually, the shape of the blue GC surface density profile is very different to that

of the galaxy surface brightness whereas both red and green GC surface density profiles

have shapes similar to the galaxy light. Both red and green density profiles are slightly

steeper than the galaxy light profile beyond ∼ 5 arcmin.

3.4 Discussion

In previous work on NGC 4365, evidence of an additional subpopulation of GCs at inter-

mediate (green) colours was based on small numbers and restricted to the central regions

(Puzia et al., 2002; Larsen et al., 2003; Hempel & Kissler-Patig, 2004; Brodie et al., 2005;

Kundu et al., 2005; Larsen et al., 2005). The large sample of GCs in this work, covering

an extended radial range, allows us to revisit these claims in the optical wavelength range.

We applied several independent statistical tests to the colour distribution, i.e. Chi-

Squared minimization (χ2), the Kolmogorov-Smirnov (KS) test and Kaye’s Mixture Model

(KMM) algorithm (see Appendix A). When the entire radial range of our imaging is used

our statistical tests cannot conclusively rule out a bimodal interpretation, partly because

GC subpopulations do not have strictly Gaussian colour distributions.

When examining GC subpopulation colours with radius we find significant negative

colour/metallicity gradients in both the bimodal and trimodal cases. Due to its radial

colour gradient, the red subpopulation has somewhat greener colours at large radii. This

explains why statistical tests showed inconclusive results when the full radial extent of

the imaging was used. However, this cannot explain the central green overdensity that

motivates splitting NGC 4365’s GCs into three subpopulations.

We compared the photometrically observable properties of blue, green and red GCs

(measured here to contain 43, 17 and 40 per cent of total GCs respectively). The high

probability blue, green and red samples have colour ranges of g− i < 0.82, 0.89 < g− i <1.0 and g − i > 1.1 where definitions are based on kernel smoothing of the GC colour

distribution. We find evidence for different properties between the subpopulations. The


green GCs are intermediate in size to the blue and red subpopulations (clearly smaller

than the blue GCs and marginally larger than the red ones) and have a significantly

different mass function to both blue and red subpopulations. The size differences could be

due to a continuous trend with colour rather than specific subpopulation properties (see

Jordan, 2004) but the differences in mass functions suggest that the three subpopulations

are indeed distinct. Green GCs also have a significantly steeper surface density profile

than red GCs within 2 arcmin (13.4 kpc) from the galaxy centre. However, the green

and red subpopulations can be described by the same radial surface density profile, with

different normalisations, beyond 2 arcmin (13.4 kpc). The ellipticity of the green GC

subpopulation is similar to the blue GC subpopulation and significantly more elongated

than that of the red. We do not detect any variation in ellipticity of the subpopulations

with galactocentric radius to 11 arcmin (74 kpc). These results lend support to the idea

that blue, green and red GCs form three distinct subpopulations.

With the same three subpopulation division we find a number of similarities between

the properties of the red subpopulation and the galaxy light of NGC 4365. The g − i ra-

dial colour gradient of the galaxy light and mean colour of the red GC subpopulation are

virtually identical (see also Spitler, 2010; Forbes & Forte, 2001). The ellipticity of the red

subpopulation is consistent with that of the galaxy starlight and the surface density profile

of the red subpopulation is closer to the starlight surface brightness profile than either blue

or green subpopulations. These results confirm similar findings in the literature (Brodie

& Strader, 2006) and support GC formation scenarios in which the red subpopulation is

closely linked to the formation of the bulk of the galaxy field stars.

To understand the physical implications of optical colour trimodality we consider the

relationship between optical colours, metallicities and ages. There is considerable debate

about whether the colour distribution of GC systems is a good proxy for the metallicity

distribution, even if uniform old ages for GCs are assumed (see discussions on the non-

linearity of colour-metallicity transformations by Yoon et al., 2006; Cantiello & Blakeslee,

2007; Hempel et al., 2007). It is also possible that one or more of the GC subpopulations

have significantly younger ages caused by a merger (see Hibbard & Mihos, 1995; Schweizer

& Seitzer, 1998; Montuori et al., 2010) though there is little spectroscopic evidence of young

ages for NGC 4365 GCs (Brodie et al., 2005).

The green subpopulation might be a consequence of a unique evolutionary history of

NGC 4365. The kinematically distinct core (KDC) in the stellar light might be a signal

of this. This property is only found in galaxies the SAURON team (de Zeeuw et al.,

3.4. Discussion 55

2002) classify as slow rotators in the inner regions (Emsellem et al., 2007). The KDC is

confined within 5 arcsec (0.56 kpc) of the galaxy centre and the stellar population inside

and outside the KDC are indistinguishable in age and metallicity. Inside 5 arcsec the

stars rotate around the minor axis and outside 5 arcsec they rotate around the major axis

(Davies et al., 2001). Davies et al. (2001) suggest that the KDC might have been formed

in the merger of “gas-rich fragments at high redshift” and state that they find no evidence

of recent star formation in NGC 4365. The effective radius of the galaxy (∼ 2 arcmin or

∼ 13.4 kpc) and the radial extent of the green subpopulation (∼ 2 arcmin) are much larger

than the KDC. It is possible that the formation of the green GC subpopulation is linked to

the formation of the KDC but since they are not spatially correlated, and other elliptical

galaxies with KDCs show no clear indication of a green subpopulation, the connection is

not clear.

The core must have been formed very early on because Davies et al. (2001) find the

same > 12 Gyr luminosity-weighted age for the KDC and the rest of NGC 4365, with no

sharp changes in metallicity across the boundary of the KDC. If the formation of the green

subpopulation is linked then we would expect the green subpopulation to also be very old.

Brodie et al. (2005) found evidence that the green subpopulation was indeed old but this

analysis was based on a small number of GCs. We might also expect to find rotation for

the green subpopulation to be offset by ∼ 90◦ compared to the other GC subpopulations

and the bulk of the starlight, if it is associated with the formation of the KDC.

Another possible signature of the unique evolutionary history could be the significant

misalignment between the galaxy’s kinematic and photometric major axes. While KDCs

are relatively common in elliptical galaxies, the minor axis rotation of the bulk of NGC

4365’s starlight is relatively uncommon. Van den Bosch et al. (2008) use triaxial orbit

based models to explain NGC 4365’s apparent minor axis rotation and KDC. The com-

bination of axisymmetry in the inner parts with triaxiality in the outer parts, which van

den Bosch et al. (2008) describe is consistent with the age and metallicity of the KDC but

bears no obvious relation to the formation of a green GC subpopulation in NGC 4365.

Alternatively, Hoffman et al. (2010) show that a ∼ 2.5 Gyr old major merger remnant with

15 − 20 per cent progenitor gas fraction has a kinematic signature remarkably similar to

NGC 4365. The gas rich major merger that Hoffman et al. (2010) describe is a tempting

explanation for the presence of green GCs because all NGC 4365’s anomalies would have

one explanation. Given that the green GC subpopulation is likely old (Brodie et al., 2005)

and there is no evidence for young stars in the KDC (Davies et al., 2001) this would have

to be a very early major merger.


The current galaxy and GC system formation scenarios that describe mechanisms for

producing bimodality in GC systems could also explain GC system trimodality for a subset

of galaxies under certain conditions.

In the multiphase collapse scenario GC systems are formed during two phases, the first

phase produces metal poor GCs, is truncated by one of a variety of possible mechanisms

and then later the second phase produces the metal rich GCs (Forbes et al., 1997). In

this context a GC system that is trimodal in metallicity could simply be the result of

three formation phases truncated twice (by the same or two different mechanisms). The

truncation of the metal poor formation phase could be caused by a universal epoch of

reionization but, for truncation to happen twice, at least one truncation mechanism has

to be related to nearby galaxies or a process internal to the galaxy, like an active galactic

nucleus.

The major merger formation scenario (Zepf & Ashman, 1993), where metal poor GCs

are present in galaxies before mergers and metal rich GCs are formed during major merg-

ers, could be consistent with some galaxies containing three GC subpopulations. If a

galaxy underwent a very early gaseous major merger and later another major merger it

may have two subpopulations more metal rich than the metal poor GC subpopulation.

Depending on the age and metallicity difference it could appear trimodal in colour.

The accretion model for GC system formation (Cote et al., 1998) found more than

two modes in some of their simulated GC systems before a third GC subpopulation was

considered for NGC 4365 from observations. Cote et al. (1998) mention that more than

two peaks are present in some of their simulations when a very steep luminosity function

(Schechter function α = −1.8) is used. They find that the peak of the metal poor popu-

lation correlates with the slope of the luminosity function and that for very steep slopes

the metal poor peak is more metal poor. It is conceivable that the merger histories of

some giant elliptical galaxies will therefore show the presence of a third, old, intermediate

metallicity subpopulation.

The questions that remain include distinguishing which of these formation scenarios

best explain the GC system of NGC 4365 and determining whether the trimodality in

the colour distribution of the GC system is reflected in the metallicity distribution. The

method in Foster et al. (2010) for determining metallicity from the Calcium triplet indices

could provide a large sample of metallicity measurements over a wide field of view to

assess whether the metallicities of the three colour subpopulations we define here are

distinct. The spectroscopic analysis of a large number of GCs could also be used to build

3.5. Summary and Conclusions 57

a picture of the kinematics of the GC system of NGC 4365 and its subpopulations. An

understanding of NGC 4365’s GC system kinematics could distinguish whether two or

three subpopulations were all formed in situ, whether the more metal rich populations

were accreted or whether the subpopulations are the result of several major mergers.

3.5 Summary and Conclusions

Combining the photometric depth, size information and resolution of HST/ACS data with

the spatial extent and three filter imaging of the Subaru/S-Cam data we can achieve a

uniquely detailed, and unmatched spatially extensive, analysis of NGC 4365’s GC system.

The GC system extends beyond 134 kpc from the galaxy centre to 9.5Re. The blue GC

subpopulation has not yet reached the background level at the very edges of our Subaru/S-

Cam imaging. We place a lower limit on the total number of GCs to be 6450± 110.

We find further evidence to support the existence of a distinct third subpopulation at

intermediate colours in the GC system of NGC 4365. We also find a trend of increasing

GC size with galactocentric radius and negative gradients in the colour/metallicity of both

blue and red subpopulations with galactocentric radius. The blue subpopulation shows

evidence for a blue tilt. Comparing the GC system with the galaxy light we find that the

red subpopulation has a similar colour, radial colour gradient, ellipticity and radial surface

density slope to the galaxy light.

Based on various measured properties we find it most likely that the green subpop-

ulation is distinct from the blue and red subpopulations. Consequently, it is very likely

that NGC 4365 has had a unique evolutionary history causing the existence of a third

GC subpopulation in this giant elliptical, where most similar galaxies only have two. The

photometric properties of the blue and red GC subpopulations are consistent with the

properties found for those of other giant elliptical GC systems, therefore any formation

scenario for the green subpopulation must leave the predicted properties of the other two

subpopulations relatively unchanged.

In Chapter 4 we will analyse the spectroscopy for NGC 4365’s GCs. Spectroscopic

analysis enables the kinematics of the GC system and it’s subpopulations to be charac-

terised, which should help determine the origin of the green subpopulation of NGC 4365’s

GC system.

4Globular cluster system kinematics and

substructure in NGC 4365

Classifying the stars has helped materially in all studies of the

structure of the universe. No greater problem is presented to the

human mind. Teaching man his relatively small sphere in the cre-

ation, it also encourages him by the lessons of the unity of Nature

and shows him that his power of comprehension allies him with

the great intelligence over-reaching all.

—Annie Jump Cannon

4.1 Introduction

Globular clusters (GCs), as some of the oldest and densest stellar systems in the Universe,

present intriguing questions about their own formation while also being very useful tracers

of the formation of their host galaxy (Brodie & Strader, 2006). They probably formed

during violent star formation episodes in their host galaxy (or host galaxy progenitors)

and therefore we can use their spatial distribution and kinematic properties to unravel

the formation history of galaxies. GCs are more numerous and more easily identifiable

in elliptical galaxies (where bright star forming clumps and dust obscuration is minimal)

than in spiral galaxies.

NGC 4365 is a giant elliptical galaxy (E3), MB = −21.3 mag (Ferrarese et al., 2006),

with various intriguing and unusual properties as highlighted below. We use these unusual

properties of NGC 4365 to constrain galaxy and GC formation scenarios by placing the

extra information contained in this system in the overall framework of ‘normal’ giant

59

http://en.wikipedia.org/wiki/Annie_Jump_Cannon

60 Chapter 4. Globular cluster system kinematics and substructure in NGC 4365

Figure 4.1: The positions of the six DEIMOS slitmasks plotted on the Subaru Suprime-Cam (S-Cam) i′ filter image. The scale is shown in the bottom left corner of the 35× 27arcmin image. It is centred on α=12:24:26.824; δ=+07:19:03.52 (J2000.0). At a distanceof 23.1± 0.8 Mpc (Blakeslee et al., 2009) 1 arcmin = 6.72 kpc.

4.1. Introduction 61

elliptical galaxies.

The GC systems of most giant elliptical galaxies are bimodal in optical colours (Brodie

& Strader, 2006; Peng et al., 2006). There is a growing body of evidence that the relative

age difference between GC subpopulations is small (Strader et al., 2005) and the distinct

colours of the commonly observed subpopulations are due to bimodality in GC metallicity

(Strader et al., 2007; Woodley et al., 2010; Alves-Brito et al., 2011) that are likely explained

by two formation mechanisms, sites or epochs. Possible formation scenarios for bimodal

metallicity distributions in GC systems are the major merger (Zepf & Ashman, 1993),

multiphase collapse (Forbes, Brodie & Grillmair, 1997) and accretion scenarios (Cote

et al., 1998). An alternative view is that the bimodal GC colour distributions can be

observed from a unimodal GC metallicity distribution, because of strong nonlinearity in

the relationship between colour and metallicity (Yoon et al., 2006; Cantiello & Blakeslee,

2007; Blakeslee et al., 2010) and in this case the kinematic properties of each colour

subpopulation are not expected to be significantly different.

The GC system of NGC 4365 has three GC subpopulations, that includes an addi-

tional subpopulation of ‘green’ GCs between the commonly observed blue and red GC

subpopulations. This additional subpopulation, thought at the time to be younger than

the ‘normal’ two subpopulations, was discovered by Puzia et al. (2002) using a combi-

nation of near-IR and optical photometry. Brodie et al. (2005) suggested that the three

subpopulations might be found as trimodality in optical colours and Larsen et al. (2005)

found that the green subpopulation was restricted to small galactocentric radii.

Specifically for NGC 4365, two independent methods have confirmed the GCs for all

three subpopulations (blue, green and red) to be older than ∼ 10 Gyr. Brodie et al.

(2005) observed a spectroscopic sample of 22 GCs to measure their ages and metallicities

using Lick indices from the Low-Resolution Imaging Spectrograph (LRIS) on the Keck

telescope. They found that NGC 4365 has three old GC subpopulations with metal poor

(blue), intermediate metallicity (green) and metal rich (red) GCs. Chies-Santos et al.

(2011) presented a near-IR photometry and optical analysis of 99 GCs with much higher

precision than previously possible, and determined that there is no observable offset in the

mean age between the GCs of NGC 4365 and GC populations of other giant ellipticals.

This indicates that there is no significant population of young GCs in NGC 4365.

In addition to its almost unique GC system, NGC 4365 has very unusual stellar kine-

matic properties. The kinematically distinct core (KDC) at its centre is relatively uncom-

mon, seen in ∼ 10 per cent of early type (E and S0) galaxies in the ATLAS3D volume

limited survey (Krajnovic et al., 2011). Also very uncommon is the starlight kinematics


outside the KDC which ‘rolls’ about the major axis rather than rotating about the minor

axis (Surma & Bender, 1995). Less than 5 per cent of galaxies, in the ATLAS3D volume

limited survey, show this kinematic behaviour. Davies et al. (2001) used the SAURON

(Spectrographic Areal Unit for Research on Optical Nebulae) integral field spectrograph

to map its kinematic and metallicity structure in two dimensions, finding stars both inside

and outside the KDC to be older than 10 Gyr. NGC 4365 is one of only two early type

galaxies, in the ATLAS3Dsample of 260, that have both a KDC and a near 90◦ misalign-

ment between the photometric and kinematic major axes (i.e. rolling rather than rotating

stars).

Finally Bogdan et al. (2012a), see also Mihos et al. (2012, in prep), presented evidence

of a stream of stellar light extending ∼ 200 kpc southwest and ∼ 100 kpc northeast from

NGC 4365. This strong indicator of a very recent ∼ 10 : 1 merger (occurring with in the

last few Gyr) presents a possible cause for the unique properties of NGC 4365.

Chapter 3 (Blom et al., 2012a) presented an optical photometry analysis of ∼ 4000

NGC 4365 GCs, using the most spatially extended sample of GCs analysed to date (ob-

served with Subaru/Suprime-Cam and supplemented with archival imaging from Hubble

Space Telescope/Advanced Camera for Surveys) and found different spatial distributions,

sizes and mass distributions for the three subpopulations of NGC 4365 GC system. They

also concluded that separate formation scenarios are required to explain the existence of

the three separate subpopulations of GC in NGC 4365.

Kinematic analysis of the three GC subpopulations in NGC 4365 is key to disentangling

the possible formation scenarios of GC systems. Several recent investigations have shown

that the kinematic features of the two standard GC subpopulations (seen in other giant

elliptical galaxies) are different (Lee et al., 2010; Arnold et al., 2011; Foster et al., 2011).

When combined with galaxy formation models this information constrains the possible

formation scenarios of GCs. For example, Foster et al. (2011) compare kinematics of the

GC subpopulations in NGC 4494 with simulations such as those described in Bekki et al.

(2005) and conclude that the galaxy has undergone a recent major merger of similar disk

galaxies. The addition of the kinematic properties of a third GC subpopulation limits the

possible formation scenarios even further.

In Section 4.2 we first describe the preparation, observation and reduction of the

spectroscopic sample of GCs and then investigate the colour/metallicity and line-of-sight

velocity distributions of the GCs. The separation of the GCs into subpopulations and

analysis of the kinematic features of the three subpopulations are presented in Section

4.3. We then discuss the results and summarise our conclusions regarding the possible

4.2. Spectroscopic sample 63

Figure 4.2: Colour distribution (reddening corrected) of GCs with radial velocity mea-surements compared with the total GC photometric sample. The grey histogram showsthe kinematic sample and the white histogram shows the distribution of photometric can-didate GCs brighter than the turnover magnitude, i′ = 23.6, and over all observable radii,∼ 20 arcmin, (scaled down by a factor of 2.7 for comparison purposes). The kinematicsample is a subsample of the photometric candidate sample. The top x-axis shows the[Fe/H] metallicity determined from the empirical conversion published in Lee et al. (2010).We note the three GC subpopulations with peaks at g′ − i′ = 0.8, 0.97 and 1.13. We alsonote that the blue GCs in the photometric candidate sample are underrepresented in thekinematic sample with respect to the number of redder GCs (g′ − i′ > 0.9).

formation scenarios for NGC 4365 in Sections 4.4 and 4.5. Appendix C contains the

kinematic fits for all the different methods of subpopulation separation we investigated,

and an alternative assumption of position angle for kinematic fitting.

4.2 Spectroscopic sample

4.2.1 Data acquisition

The GC spectroscopic sample presented in this work was obtained from six multi-object

DEIMOS slitmasks observed in clear dark time on the Keck II telescope. This galaxy

was observed as part of the SAGES Legacy Unifying Globulars and Galaxies Survey


(SLUGGS1) (Brodie et al. 2012 in prep). Four masks were observed on 2010, Jan-

uary 11 and 12 with seeing between 0.6 and 0.9 arcsec and a further two masks were

observed on 2010, February 18 with seeing of ∼ 0.85′′. In Chapter 3 (Blom et al., 2012a)

Subaru/Suprime-Cam (S-Cam) and Hubble Space Telescope / Advanced Camera for Sur-

veys (HST/ACS) imaging was used to identify GC candidates associated with NGC 4365

and publish extinction corrected g′, r′ and i′ magnitudes for S-Cam and/or g and z mag-

nitudes for HST/ACS candidates. A total of 443 GC candidates with i′ brighter than 23

mag were placed in slits, for which we aimed to obtain a spectrum of high enough signal-

to-noise (S/N ∼ 5) to determine a line-of-sight radial velocity. For all six masks a 1200 l

mm−1 grating was used, with 1 arcsec wide slits and centred on 7800 A to obtain ∼ 1.5

A resolution. This set up (see also Foster et al., 2011; Arnold et al., 2011; Strader et al.,

2011; Romanowsky et al., 2009, 2012) allows observations from ∼ 6550 - 8900 A , which

covers the wavelength range in which we expect the red-shifted Calcium II triplet (CaT)

absorption features (8498, 8542 and 8662 A). It sometimes also includes the red-shifted

Hα line (6563 A) at the blue end of the observed spectrum.

Footprints of the six DEIMOS masks plotted on the S-Cam i′ filter image, used to

identify GCs around NGC 4365, are shown in Fig. 4.1. We obtained total exposure times

of 110, 40, 105, 96, 120 and 75 minutes for each of the six masks respectively. For each

mask the total exposure time was split into three or four separate exposures to minimize

the effects of cosmic rays. The total exposure times for each mask were independently

constrained by seeing and object visibility during the night. The median seeing was 0.8,

0.7, 0.75, 0.7, 0.9 and 0.7 arcsec for each of the six masks respectively. Masks two and six

were observed at the end of their respective observing nights.

The raw data were reduced to one dimensional spectra using a customised version

of the deep2 (Deep Extragalactic Evolutionary Probe 2) galaxy survey data reduction

pipeline (idl spec2d; Cooper et al., 2012; Newman et al., 2012). The pipeline uses

dome flats, NeArKrXe arc lamp spectra and sky light visible in each slit to perform flat

fielding, wavelength calibration and local sky subtraction, respectively. It outputs the

object spectrum as well as the locally subtracted sky spectrum.

By placing the slits at an angle of at least 5◦ offset from the position angle of each mask

the deep2 pipeline is forced to determine an individual wavelength calibration for each

pixel column of the spectra in each slit. This ensures that the root-mean square of the

wavelength calibration is as small as possible, Newman et al. (2012) state that the deep2

pipeline achieves a root-mean-squared (rms) accuracy of 0.007A, with adequate arclamp

1http://sluggs.swin.edu.au/


exposure and a fifth order Legendre polynomial iterative fit. This rms is determined from

the final mean residual about the fit. The 5◦ tilt in the slit also improves the accuracy of

the sky subtraction, by oversampling each sky line to allow a more accurate fit to the sky

level.

4.2.2 Obtaining line-of-sight velocities

We obtained reliable line-of-sight radial velocities for 252 GCs. This increases the previ-

ously available NGC 4365 spectroscopic GC sample (33 individual GCs in Larsen et al.,

2003; Brodie et al., 2005, combined) by an order of magnitude.

The line-of-sight radial velocity is determined by the peak of the cross correlation

function (obtained using the IRAF task RV.FXCOR) between the spectrum and 13

stellar templates. The velocity measurement of a GC is considered reliable if the correlation

function has a single, identifiable peak and at least two of the four absorption lines (three

CaT and one Hα) are clearly visible in the red-shifted spectrum. In a few cases one or

other of these visual checks was not convincing and the resulting GC velocity is recorded

as a borderline velocity measurement (17 objects in total). The final catalogue of 269

radial velocities for NGC 4365 GCs are given in Pota et al. (2013).

The colour distribution of the kinematic spectroscopic sample is compared with that

of the photometric sample in Fig. 4.2. The photometric sample includes GC candidates

with g′− i′ colours between 0.5 and 1.4, showing overdensities at g′− i′ ∼ 0.8 and between

0.95 and 1.2 as well as an underdensity at g′ − i′ ∼ 0.9. Ideally the kinematic sample will

have the same colour distribution, scaled down in number, as the photometric sample. In

this ideal case we would be able to extrapolate conclusions drawn from the spectroscopic

samples to the entire GC population without caveat. Fig. 4.2 shows that while we do not

completely achieve this ideal, the spectroscopic sample does cover the full colour range and

shows a significant underdensity at intermediate colours between the blue and the green

subpopulations (seen at g′ − i′ ∼ 0.9). In the spectroscopic sample blue GCs are under

represented with respect to the green/red GCs compared to the photometric sample. This

is because the six DEIMOS pointings have a much smaller radial extent (∼ 12 arcmin)

than the S-Cam imaging (∼ 20 arcmin) and at larger galactocentric radii there are many

more blue GCs than red or green GCs (see Fig. 4.5 and Section 3.3.5.

We expect to see a roughly Gaussian distribution in the line-of-sight velocities of GCs

around NGC 4365. In Fig. 4.3 we show a Gaussian distribution overplotted on the velocity

distribution of NGC 4365’s GCs. The peak is set to the literature value for NGC 4365’s

recession velocity (vsys = 1243 km s−1, Smith et al., 2000) and the standard deviation is


Figure 4.3: Line-of-sight velocity distribution for GCs in the kinematic sample. Thedistribution of the secure line-of-sight velocity measurements (252 GCs) is plotted with asolid line and the borderline velocity measurements (17 GCs) are added to the distributionwith a dashed line. The plotted Gaussian distribution has a peak at 1243 km s−1 and avelocity dispersion of 237 km s−1. The velocity distribution of the GCs agrees well withthe Gaussian at high velocities but is skewed to low line-of-sight velocities.


Figure 4.4: Line-of-sight radial velocity (Vlos) of GCs plotted against galactocentricradius. Filled black circles show the 252 GCs with reliable radial velocity measurements,while grey circles show borderline GC detections. The solid black line is plotted at thegalaxy systemic velocity Vsys = 1243 km s−1, dotted lines show 1σ and 2σ envelopes andthe dashed line shows the 3σ envelope. The σ values were calculated in bins of 29 GCswith Vlos > 1243 km s−1, excluding borderline GCs. Note the large number of low velocity(Vlos ∼ 700 km s−1) > 2σ outliers. The solid grey line is plotted at the systemic velocityof the nearby galaxy NGC 4342 (Vsys = 751 km s−1, Grogin et al., 1998). which lies ∼ 35arcmin from NGC 4365


Figure 4.5: GC surface density with colour and galactocentric radius. The colour scaleindicates object number per arcmin2 per colour bin ranging from black at low densityto red at high density. The colour-radius position of spectroscopically confirmed GCs areoverplotted with white dots. GCs with radial velocities more than 2σ from the host galaxyare marked with black plusses (positive) and dots (negative). The distribution of GCswith observed line-of-sight radial velocities is consistent with the underlying distributionof photometrically observed GCs in the same radial range and there is no indication thatthe GCs with > 2σ low velocities deviate from the underlying distribution either.

set to 237 km s−1. This standard deviation was calculated from one side of the velocity

distribution, i.e. from 68 percent of the GCs with Vlos > 1243 km s−1. The peak agrees

well with the observed GC velocity distribution but an excess of GCs is seen above the

overplotted Gaussian at low velocities in Fig. 4.3.

We can calculate the skewness (ξ3) of the velocity sample using the standardised third

moment (Joanes & Gill, 1998),

ξ3 =

√N(N − 1)N(N − 2)

∑Ni=1(xi − x)3

s3(4.1)

where N is the total number of GCs in the sample, xi is the line-of-sight velocity of the

ith GC, x is the mean line-of-sight velocity of the sample and s is the standard deviation

of the sample. The calculated skewness is not strongly dependent on whether the en-

tire GCs sample (including borderline velocity measurements) or only the secure velocity

measurements are used,

ξ3,all = −0.20

ξ3,secure = −0.19

The standard error in skewness (εξ3) can be calculated from the number of GCs in the


sample (Joanes & Gill, 1998),

εξ3 =

√6N(N − 1)

(N − 2)(N + 1)(N + 3)= 0.15 (4.2)

The measured negative skewness of the GC line-of-sight velocity distribution (−0.19±0.15)

is only marginally significant, given the relatively small number of GCs contributing to

the skewness. We further investigate the possible causes of this negative skewness in the

velocity distribution.

4.2.3 Low velocity GCs

We explore the possibility that the GCs causing marginal skewness in the measured ve-

locity distribution are associated with the stream of stars that were found crossing NGC

4365 (North East to South West) from analysis of wide-field, very deep B filter imaging

(Bogdan et al. 2012a; Mihos et al. 2012, in prep). The stream seems to be an indication

of an ongoing merger with a small galaxy. The distinct velocities of these GCs may in-

dicate that they are not associated NGC 4365’s GC system and the recently discovered

stream is a likely candidate. Fig. 4.4 shows the GC line-of-sight velocities plotted against

galactocentric radius. To define which GC velocities are outliers we used the GC velocities

> 1243 km s−1 (where the velocity distribution is not affected by skewness) to calculate

the standard deviation of the sample, σ. The calculation of σ was done in radial bins of

29 GCs each (excluding borderline velocity measurements). The calculated 1σ, 2σ and 3σ

envelopes are plotted above and below the systemic velocity and show clearly that there

is a larger number of GCs with low velocities outside the 2σ envelope (13) than with high

velocities (3).

When we plot these velocity outliers on a map of GC surface density with colour and

galactocentric radius there is no strong indication that the low velocity outliers follow a

separate distribution to the colour-radius distribution of NGC 4365 GCs (see Fig. 4.5).

The density map shows the distribution of photometrically identified GCs overplotted

with the kinematic sample of GCs (those with reliable radial velocities). The blue and red

subpopulations, as well as an overdensity of green GCs in the central regions, are visible in

the density plot and the GCs with radial velocity measurements follow the colour-radius

distribution well. The low velocity outliers tend to have red or green colours in the inner

regions and blue colours in the outer regions of the galaxy, which is consistent with the

general distribution of NGC 4365 GCs in colour-radius space.

In Fig. 4.6 the positions of the low velocity GCs are plotted on a map of the spatial


Figure 4.6: Spatial distribution of GCs showing individual radial velocities. The colourof individual points correspond to their radial velocity and the scale runs from 500 (darkblue) to 1800 km s−1 (dark red). GCs with radial velocities more than 2σ from the hostgalaxy are marked with plusses (positive) and dots (negative).

4.3. Kinematics of the GC subpopulations 71

distribution of the GCs with line-of-sight velocities. All but two of the low velocity GCs

form a line running NE to SW across NGC 4365. This spatial structure of low velocity

GCs is aligned with the recently discovered tidal stream (Bogdan et al. 2012a; Mihos et

al. 2012, in prep) and would seem to indicate that the majority of the low velocity GCs

are associated with the stream.

In summary, the evidence does not conclusively show that the low velocity GCs are

associated with the stellar stream. Their distribution in colour-radius space is not distin-

guishable from the overall distribution of NGC 4365 GCs but their spatial distribution

does suggest that they are different from the bulk of NGC 4365 GCs. We exclude all GCs

with line-of-sight velocities that lie outside the 2σ envelopes and GCs with borderline

velocity measurements from further kinematic analysis. This conservative sample ensures

that the kinematic results are unaffected by unreliable GC velocity measurements and that

the sample is not skewed to low velocities. We note here that including the low velocity

outliers makes no significant difference to subsequent kinematic results, except to increase

the uncertainties in our fits.

4.3 Kinematics of the GC subpopulations

4.3.1 Kinematic model description

To investigate the detailed kinematic properties of the three GC subpopulations we fit an

inclined disk model to the GC system of NGC 4365, using the individual GCs as tracers.

This inclined disk model is able to fit for rotation velocity and the position angle of that

rotation with our sparse and non-uniform sampling of tracer particles. To fit this model

we minimize (as in Foster et al., 2011);

Λ =N∑i=1

[(Vobs,i − Vmod,i)2

σ2 + (∆Vobs,i)2+ ln(σ2 + (∆Vobs,i)2)

](4.3)

Vmod,i = Vsys ±Vrot√

1 +(

tan(PAi−PAkin)qkin

)2(4.4)

where Vobs,i is the observed line-of-sight velocity, ∆Vobs,i is the uncertainty on the

observed velocity, σ is the fitted velocity dispersion of the sample, Vmod,i is the modelled

velocity given by Equation 4.4, Vsys is the recession velocity of NGC 4365, Vrot is the

rotation velocity fitted to the sample, PAi is the position angle of the ith GC, PAkin is the

kinematic position angle fitted to the sample and qkin is the kinematic axis ratio (b/a) fixed


for the sample. In contrast to Foster et al. (2011) we do not use a minimisation algorithm

to determine the minimum value of Λ but evaluate the function for all reasonable values

of Vrot, σ and PAkin. This brute force method ensures that we have found the global

minimum for the function and therefore the best fitting values.

We use the bootstrap Monte Carlo method to obtain an estimate of the uncertainty of

the model fit. A random sample of GCs is chosen from the original GC sample (equal in

number to the original sample but allowing the data from a given GC to be used more than

once) and then Λ is minimized for that randomised sample. This is repeated 1000 times and

the uncertainties are determined by calculating the central 68 per cent confidence intervals

of the distribution (1σ) for each of Vrot, σ and PAkin. To use a brute force minimisation

method for each iteration would be prohibitively time consuming and therefore we use a

Powell minimisation algorithm (from SCIPY.OPTIMIZE in the PYTHON framework)

with initial estimates set to the solution obtained from the kinematic best fitting values.

The Powell minimisation algorithm is marginally sensitive to the initial conditions but we

found no significant difference in the uncertainties obtained using this method and those

found for a test case where the brute force method was used instead.

In general the 1σ uncertainties are not symmetric about the best fitting value because

the distribution obtained from the bootstrap algorithm is not symmetric about the peak

value. We calculate a central 68 per cent confidence interval rather than a symmetric one.

The peak value of the distribution aligns well with the best fitting value in most cases.

We do not artificially align the peak value of the distribution with the best-fit value and

consequently it is possible for the best fitting value to fall outside the 1σ range of the

distribution. This is a clear indication that for such a case the model is inappropriate for

the data.

We use this model to determine the kinematic properties of NGC 4365’s GC system

as a whole, finding:

Vrot = 39.5±42.310.5 km s−1,

σ = 212.2±7.49.2 km s−1 and

PAkin = 147.3±22◦26 .

This would seem to indicate a GC system with a defined but small rotation along

the photometric minor axis of the galaxy (132◦). However it is well-known that GC

subpopulations within a single galaxy can show different rotation properties (Lee et al.,

2010; Arnold et al., 2011; Foster et al., 2011) and might confuse any rotation signal unless

they are separated, therefore we explore further.


Figure 4.7: Kinematics for NGC 4365 GCs as a function of g′ − i′ colour. Kinematicsare fitted for a rolling sample of 30 GCs and are plotted at the mean colour of the sample.The top panel shows the fitted rotation velocity (Vrot), the centre panel shows the fittedvelocity dispersion (σ) and the bottom shows the fitted kinematic position angle (PAkin).Solid lines show the kinematic fit to the data and the grey filled area shows the 1σ un-certainties (obtained from 1000 bootstrap trials). On the bottom panel the photometricmajor axis of NGC 4365 (see Section 3.2) is plotted with dashed lines and the minoraxis is plotted with dotted lines. The bottom panel shows that the very blue, green andvery red GCs all have constrained position angles of rotation and that the blue and redsubpopulations rotate in a different direction to the green subpopulation. Where there issignificant contamination between subpopulations in colour the rotation is unconstrained.

4.3.2 Kinematics as a function of colour

We use our kinematic model to analyse the kinematics of NGC 4365’s GCs as a function

of colour. The GCs with velocities less than 2σ away from the galaxy systemic velocity

and secure velocity measurements are used to form a rolling sample of 30 GCs as follows.

The 30 GCs with bluest colours form the first subsample. To form the next subsample the

bluest GC is discarded while the GC with the bluest colour that was not included in the

first subsample (i.e. the next reddest) is added and this process is repeated until all GCs

have been included in a subsample. We chose the sample size of 30 as a best compromise

between samples large enough to robustly constrain the kinematics and samples small

enough to give adequate colour resolution. The kinematic fitting procedure is carried out


on each subsample and the results are shown in Fig. 4.7. In this case we fix the kinematic

axis ratio to one (qkin = 1) as the photometric axis ratio for the galaxy light as well as blue,

green and red GC subpopulations are all different (qblue = 0.56, qgreen = 0.70, qred = 0.97

and qgal = 0.75 from Section 3.3.5). This assumption of qkin = 1 is equivalent to assuming

a circular rotation disk and is chosen as the simplest case scenario.


Sp

lit

Vrot

σP

Akin

Vrot

σP

Akin

Vrot

σP

Akin

Defi

nit

ion

(km

s−1)

(km

s−1)

(◦)

(km

s−1)

(km

s−1)

(◦)

(km

s−1)

(km

s−1)

(◦)

med

ium

pro

bab

ilit

yPBlue>

0.8

(53)

PGreen>

0.8

(53)

PRed>

0.8

(65)

49±

48

18

179±

10

17

254±

38

54

83±

57

29

229±

13

22

145±

33

31

39±

52

11

218±

14

22

174±

62

53

colo

ur

cut

g′ −

i′<

0.85

(61)

0.9<g′ −

i′<

1.05

(77)

g′ −

i′>

1.1

(56)

67±

44

27

178±

10

16

269±

28

34

81±

39

24

217±

10

15

144±

27

23

94±

48

34

202±

15

26

194±

23

24

Tab

le4.

1:Su

mm

ary

ofki

nem

atic

fits

tova

riou

sdi

visi

ons

ofth

eth

ree

GC

subp

opul

atio

ns.

We

tabu

late

the

rota

tion

velo

city

(Vrot)

,ve

loci

tydi

sper

sion

(σ)

and

kine

mat

icpo

siti

onan

gle

(PAkin

)fo

rea

chsu

bpop

ulat

ion

inth

em

ediu

mpr

obab

ility

(pro

babi

lity

deno

ted

wit

hPColour)

and

best

colo

urcu

tsp

lits.

Num

bers

ofG

Cs

inea

chsu

bpop

ulat

ion

repr

esen

tati

vesa

mpl

ear

egi

ven

inbr

acke

tsaf

ter

the

defin

itio

npa

ram

eter

s.T

hem

ajor

axis

ofN

GC

4365

’sst

ella

rlig

htis

42◦

or22

2◦.


Fig. 4.7 shows that the GCs of NGC 4365 have significantly different kinematic fea-

tures, which depend on GC colour. The changes in position angle seen in Fig. 4.7 show

this very clearly, e.g. blue GCs rotate with PAkin that is consistent with the photometric

major axis, green GCs rotate with PAkin consistent with the photometric minor axis and

red GCs with rotate with PAkin that is between the photometric major and minor axes

of NGC 4365. At the colours where we expect significant overlap between subpopulations

the rotation cannot be constrained. Here the values fitted for the PAkin become unsta-

ble while the values fitted for Vrot drop to zero and become inconsistent with the errors

calculated. Where it is not possible to constrain the position angle of the rotation, the

amplitude of rotation is also unconstrained.

Foster et al. (2011) noted that Vrot is artificially increased when the PAkin is allowed

to vary. Strader et al. (2011) describe this artificial increase as a rotation bias and note

that this bias is important when the amplitude of rotation is low, e.g.

Vrotσ

6 0.55

√20N

(4.5)

Here N denotes the number of GCs in the sample. We apply this criterion to the kinematic

fit as a function of colour of NGC 4365’s GCs. Where the PAkin is well constrained

(g′ − i′ ∼ 0.79− 0.84, 0.97− 1.02 and 1.12− 1.17) the rotation parameter (Vrot/σ) is well

above the criterion value for this sample size (N = 30). We also see that the rotation

parameter fails the criterion (i.e. is affected by rotation bias) in areas where the PAkin is

poorly constrained (e.g. 0.87 < g′ − i′ < 0.95) even when the fitted Vrot is measured to be

significantly greater than zero.

In the blue, green and red colour ranges where we do constrain the kinematics the blue

subpopulation Vrot and σ ranges are 50− 140 and 140− 160 km s−1 respectively while the

green subpopulation Vrot and σ ranges are 80 − 200 and 180 − 220 km s−1 respectively.

The rotation range of the red subpopulation is Vrot ∼ 80 − 180 km s−1 while its velocity

dispersion decreases with increasingly red GC colour, σ ∼ 220 to ∼ 200 km s−1.

This observation of distinct kinematic features is a confirmation that the three GC

subpopulations of NGC 4365 seen in colour and other photometric properties (Larsen et al.,

2003; Brodie et al., 2005; Blom et al., 2012a) are indeed separate. They are likely to have

formed via different mechanisms or at different epochs. To better constrain the position

angle (PAkin) and derive clearer values for the amplitude of rotation (Vrot) and dispersion

(σ), we need to divide the GCs into three uncontaminated representative samples of the

three GC subpopulations.


Figure 4.8: Observed line-of-sight velocity is plotted against position angle for the blue(left), green (centre) and red (right) GC subpopulations. We show the subpopulationsas defined by the best colour cut split (see Table 4.1). The fitted rotation velocity is plottedwith a solid sinusoid, and an envelope of twice the fitted velocity dispersion is shown withdotted lines for each subpopulation. The position angle (PAkin) of rotation is 269◦, 144◦

and 194◦ for the blue, green and red subpopulations respectively. The photometric majoraxis of the galaxy is 42/222◦ and the overdensity of blue GCs at ∼ 210◦ is due to thestrong elongation of the blue GC subpopulation along the major axis of the galaxy.

4.3.3 Dividing the sample into three subpopulations

In Chapter 3 we divided NGC 4365’s GCs into three subpopulations according to a proba-

bility of belonging to that subpopulation. Each GC was assigned a probability of belonging

to the blue, green and red subpopulations (PBlue +PGreen +PRed = 1) based on its colour

and galactocentric radius. To quote, “The process used to determine the blue-green-red

probability of a GC starts by determining the relative number of GCs in each subpopula-

tion at the galactocentric radius of the GC. This is done by comparing the values of the

radial surface density profile for each subpopulation. The relative numbers of GCs in each

population are used to scale the normalisations of the three Gaussian distributions and

comparing the relative values of all three Gaussian distributions at the colour of the GC

we calculate the probability of an object being blue, green, or red.” The high probability

subsamples (P > 0.95) do not include any green GCs and therefore we define medium

probability samples with P > 0.80 to represent the three subpopulations. Of GCs with

radial velocities, 53 blue, 53 green and 65 red GCs form part of the medium probability

samples.

The kinematic fits to the medium probability fits are shown in Table 4.1. The blue and

green sample fits agree with the kinematic properties found in Fig. 4.7 but the fit to the

red sample is not well constrained (PAkin varies by 115◦).The blue medium probability

sample contains GCs with colours bluer than g′− i′ ∼ 0.85 and the green sample contains

GCs with colours between g′ − i′ ∼ 0.85 and g′ − i′ ∼ 1.05. These samples contain


relatively few GCs but do not overlap and are mostly free of contamination from the other

subpopulation. The red medium probability sample contains more GCs but also contains

GCs with colours as ‘blue’ as g′−i′ ∼ 1.0 and is likely affected by contamination from green

GCs. We expect some contamination particularly in the red sample because the medium

probability red GCs extend almost as far as the peak colour of the green subpopulation

(g′− i′ = 0.98). The probability that these GCs belong to the green subpopulation is close

to 20 percent and not trivial.

We also experimented with splitting the GCs into three subsamples at different g′ − i′

colours, detailed in Appendix C. To determine the most accurate kinematics for the three

colour subpopulations of NGC 4365 we need to include as many GCs as possible in each

sample while minimising the number of GCs from one subpopulation contaminating the

sample for another subpopulation. The best kinematic results are determined by the

fit which has the minimum value of Λ/ndf (see Equation 4.3) and the most constrained

PAkin. Salient results are shown in Table 4.1 and details discussed in Appendix C. The

best kinematic fits are found for the 61 blue GCs with g′ − i′ < 0.85, 77 green GCs with

0.9 < g′ − i′ < 1.05 and 56 red GCs with g′ − i′ > 1.1. The results for the medium

probability and best colour cut samples are consistent within the uncertainties, but the

best colour cut sample contains larger numbers of blue and green GCs and a relatively

uncontaminated sample of red GCs. The red sample in this case ends close to the expected

peak in the red subpopulation (g′−i′ = 1.13). Henceforth we use the best colour cut sample

for the subpopulation representative samples.

The kinematics are fitted twice for each colour split definition, once with the axis ratio

fixed to the photometric value for each GC subpopulation (qkin = qphotom) and once with

the axis ratio fixed to unity (qkin = 1) as before. We do not have enough GCs in each

sample to fit for qkin and it is not clear that the kinematic axis ratio would necessarily be

equal to the photometric axis ratio, especially since Fig. 4.7 leads us to expect rotation

in different directions to the photometric position angle. We do not find any significant

differences in the fitted kinematics between the two axis ratio cases and conclude that this

kinematic fitting method is not very sensitive to the kinematic axis ratio (see Appendix

C). All further kinematic fits are done in the case where qkin = 1 as this is the more

general case.

We compare the rotation of these subpopulation representative samples (henceforth

referred to as the blue, green and red subpopulations) with each other and with the

kinematics of the central starlight (to ∼ 0.5 arcmin) as measured by Davies et al. (2001)

and Krajnovic et al. (2011). They found that the KDC rotates with a PAkin of 38 ± 5◦


aligned with the photometric major axis of the galaxy (42◦) and that the bulk of the

starlight rotates with a PAkin of 145 ± 6.5◦, very close to the minor axis of the galaxy

(132◦). Using the Stellar Kinematics from Multiple Slits (SKiMS) technique (see also

Proctor et al., 2009; Foster et al., 2011), Arnold et al. (2012, in prep) find that the

minor axis rotation of the starlight continues out to ∼ 4.5 arcmin. We find that the blue

subpopulation rotates with a PAkin between 235◦ and 297◦, not consistent with either the

major (222◦) or the minor (312◦) axes of NGC 4365. The green subpopulation rotates

with a PAkin of 144±27◦23 , consistent with the minor axis of the galaxy and the rotation of

the bulk of the starlight. The red subpopulation rotates with a PAkin between 170◦ and

217◦, almost consistent with the major axis but in the opposite direction to the stars of

the KDC. The kinematic properties that we have found by using the best representative

samples for each GC subpopulation agree with the kinematics seen in Fig. 4.7 where GCs

kinematics are plotted as a function of colour.

Fig. 4.8 shows the combined rotation velocity and velocity dispersion. These are

plotted separately for each subpopulation and the best-fit models show good agreement

with the observed radial velocities of the subpopulations. The non-uniform azimuthal

distribution of GCs, seen particularly in the blue subpopulation, but also in the green

subpopulation, is partly due to the elliptical spatial distribution of these subpopulations

(axis ratio qblue = 0.56 and qgreen = 0.70, see Section 3.3.5) and partly due to the asym-

metric spatial coverage of the spectral observations. We do not expect this azimuthal bias

to significantly influence the kinematic fits.

4.3.4 Radial kinematics for three subpopulations

We use the best colour cut samples to analyse the variation of kinematics with galacto-

centric radius for each subpopulation. The samples contain 61 and 56 GCs for the blue

and red subpopulations respectively, but only 61 of 77 GCs for the green subpopulation

as we restrict the green sample to include only GCs with galactocentric radii smaller than

5 arcmin. We impose this galactocentric radius restriction on the green subpopulation

because beyond this radius it contributes less than ten per cent of the total GC popula-

tion. The GC subpopulation kinematics are fitted as a function of galactocentric radius in

rolling bins of 22 GCs and the results of the kinematic fits are shown in Fig. 4.9. The bin

size of 22 GCs was chosen to maximize the radial range of the kinematic analysis. This

is significantly smaller than the bin size used for Fig. 4.7 because inter subpopulation

contamination has been reduced. We fit the kinematics in the case where the PAkin is

allowed to vary with galactocentric radius.


Figure 4.9: Kinematics as a function of radius for NGC 4365’s three GC subpopulations.GC subpopulations are defined using the best colour cut in Table 4.1. Kinematics arefitted for a rolling sample of 22 GCs and are plotted at the mean galactocentric radiusof the sample. The top panel shows the fitted rotation velocity (Vrot), the centre panelshows the fitted velocity dispersion (σ) and the bottom panel shows the fitted kinematicsposition angle (PAkin). The blue subpopulation (all GCs bluer than g′ − i′ = 0.85) isplotted with blue dashed lines, the green subpopulation (GCs between g′ − i′ = 0.9 and1.05 with galactocentric radius < 5 arcmin) is plotted with green dash-dotted lines andthe red subpopulation (all GCs redder than g′ − i′ = 1.1) is plotted with red dotted lines.The lines show the kinematic fit to the data and the filled areas show the 1σ uncertainties(obtained from 1000 bootstrap trials). The black points show stellar kinematics extractedalong the minor axis of NGC 4365 from the SAURON data (de Zeeuw et al., 2002). Theeffective radius of the galaxy starlight is plotted with a vertical line at 2.1 arcmin. On thebottom panel the photometric major axis of NGC 4365 is plotted with dashed lines andthe minor axis is plotted with dotted lines. The variation of the PAphot is less than 3◦

and not visible on this scale. The kinematic fits to the three subpopulations show clearlydifferent features.


The blue subpopulation shows significant rotation at all radii. There is an indication

that the PAkin of rotation twists from ∼ 200◦ to ∼ 310◦ between 3 and 7 arcmin (20

and 47 kpc). The rotation velocity is consistent with being flat and Vrot ∼ 100 km s−1

over the entire radial range of the blue subpopulation. The velocity dispersion of the blue

subpopulation is also consistent with being flat and σ ∼ 200 km s−1. The radial kinematic

profiles are consistent with the values found for the blue GCs as seen in Fig. 4.7 as well as

the fitted values for the entire blue subpopulation sample (Table 4.1). The kinematics of

the blue GC system varies more significantly with galactocentric radius than GC colour and

by removing most of the contaminating GCs coming from the green subpopulation as well

as dividing the blue GC by radius, we achieve tighter constraints on the subpopulation

kinematics. The twist in the PAkin could suggest that the radial outskirts of the blue

subpopulation have been affected by accretion of galaxies and associated GCs, or a tidal

interaction. This kinematic twist could also be a signature of triaxiality in the blue GC

subpopulation.

The green subpopulation rotates relatively fast at small radii (Vrot ∼ 200 km s−1) but

the rotation decreases quickly with increasing galactocentric radius to ∼ 75 km s−1 at 3.5

arcmin. The radially fitted PAkin is consistent with being flat (150± 30◦) and consistent

with the PAkin fitted to the entire subpopulation (144±27◦23 ). The velocity dispersion of

the green subpopulation also decreases with galactocentric radius from 260 to 180 km s−1.

The red subpopulation rotates fastest between 2 and 4 arcmin, with PAkin ∼ 222◦. The

rotation velocity amplitude here is Vrot ∼ 180 km s−1.The fitted PAkin within 2 arcmin is

not as well constrained as that fitted between 2 and 4 arcmin, and within 2 arcmin the

rotation parameter fails the criterion defined in Equation 4.5. The rotation of the red GC

subpopulation at small galactocentric radii is consistent with zero rotation and the value

of ∼ 75 km s−1 fitted here is due to rotation bias caused by the free variation of PAkin.

The velocity dispersion of the red GCs decreases with radius from σ ∼ 220 to ∼ 150 km

s−1. The red subpopulation is dispersion dominated in the central regions and rotation

dominated in the outer regions of the galaxy.


Figure 4.10: Kinematic parameters of GCs as a function of galactocentric radius. Rota-tion parameter Vrot/σ (top) as well as the root mean squared velocity VRMS (bottom)for GC subpopulations plotted against galactocentric radius. Linestyles are the same asthose in Fig. 4.9. This figure shows the case where the position angle is allowed to vary.The rotation bias is small everywhere except inside a galactocentric radius of 2 arcminfor the red subpopulation. Here the plotted value for Vrot/σ is significantly overestimatedbut because there is no fitting involved in obtaining the VRMS this value is not affected.

4.3. KinematicsoftheGCsubpopulations 83

gi

(ma

g)P

Ap

hot

om

(◦)

PA

kin

(◦)

Vr

ot(k

ms−

1)

σ(k

ms−

1)

qp

hot

Re

(arc

min)

Blue

GCs

0.80

†∼

42/2

22†,

126

9±28

34

67±

44

27

178±

10

16

0.5

6±

0.08

†7.3

0±

0.68

†

Gree

nG

Cs0.

97†

∼42

/222

†,1

144±

27

23

81±

39

24

217±

10

15

0.7

0±

0.08

†∼

2.97

†,2

Re

dG

Cs1.

13†

unde

fine

d19

4±23

24

94±

48

34

202±

15

26

0.9

7±

0.08

†3.1

7±

0.19

†

Star

s1.

15†

42/2

22±

3†

145

±6.

560

.9,4

260

±10

•,4

0.7

5±

0.03

†2.1

±0.1

1†

KD

C1.

40,3

40.9

±2.

138

±5

•63

±8

•,4

262

±3

•,4

0.7

5±

0.08

†0.1

2±

0.0

2•,5

Ta

ble

4.2:

Su

mma

ry

ofthe

NG

C43

65ga

laxy

sys

tem

prope

rtie

s.Va

lue

sma

rke

dwi

th

†ar

eta

kenfr

om

Cha

pter

3,fr

om

Kraj

novi

cet

al.

(201

1),

fro

mFe

rrar

ese

etal.

(200

6)and

•fr

om

Davi

eset

al.

(200

1),

while

all

othe

rva

lue

sar

ede

rive

din

thi

sCha

pter

.No

tes:

1The

pos

itio

nang

les

for

the

blue

and

gree

nsubpopul

atio

nswe

requa

lita

tive

ly

simila

rto

the

gala

xyli

ght

and

were

the

refo

reas

su

med

equa

lto

it.

2Thi

seffe

ctive

radi

usme

asur

eme

ntis

not

obt

aine

ddi

rect

ly

fro

ma

Sers

icpr

ofil

efit

tothe

gree

nsubpopul

atio

nbut

by

compa

ring

the

po

wer

law

slope

ofthe

gree

nand

red

subpopul

atio

ns.

3The

g−

zva

lue

was

conv

erte

dto

ag

−i

colo

urva

lue

.4The

sear

ema

ximu

mve

loci

ties

obs

erve

din

ali

mite

dfie

ld-

of-vi

ew

not

mean

orme

dian

velo

citi

es.

5Thi

sis

the

larg

est

radi

alext

ent

ofthe

KD

Cand

not

its

effe

ctive

radi

us.


Plotting the rotation parameter (Vrot/σ) in Fig. 4.10 we see that the red subpopulation

shows very different properties to the blue and green subpopulations. This ratio is one

measure of the diskyness of the system, where Vrot/σ > 1 can indicate a disky system. It

is only the red GC subpopulation that shows rotationally dominated characteristics, and

only at galactocentric radii beyond 2.5 arcmin, where it rotates with a PAkin of ∼ 222◦

(photometric major axis of NGC 4365).

The root mean squared velocity(VRMS ∼

√P(Vlos−Vsys)2

N

)shows variation with ra-

dius for all three subpopulations but changes most significantly in the green subpopulation.

The green GC subpopulation VRMS decreases from ∼ 270 to ∼ 190 km s−1 over 1.5 ar-

cmin in galactocentric radius. The sharply declining VRMS seen in the case of the green

subpopulation could be explained by a truncation in their density distribution or a bias

towards radial orbits relative to the other GC subpopulations.

4.4 Discussion

Since most giant elliptical galaxies contain only two metallicity subpopulations of GCs it

is likely that one of NGC 4365’s three GC subpopulations is the result of an additional

and unusual formation/accretion process. Determining which of the GC subpopulations is

additional may indicate which mechanism caused it and could be a key to uncovering the

formation mechanisms of GC subpopulations in ordinary elliptical galaxies. We summarise

the kinematic and photometric properties of the three GC subpopulations as well as the

two main components of the galaxy starlight (the KDC and the bulk of the stars) in Table

4.2 and use these properties to discuss possible formation scenarios.

We find three distinct kinematic signatures for the three GC subpopulations. The

blue subpopulation rotates at all observable radii and its kinematic position angle twists

slowly with increasing galactocentric radius from 200◦ to 312◦. Its rotation is unrelated to

NGC 4365’s stellar light. The green subpopulation rotates with the bulk of NGC 4365’s

stellar light along the photometric minor axis of the galaxy (132◦). The red subpopulation

rotates only at large radii along the photometric major axis (222◦) of NGC 4365 but in

the opposite direction to the KDC of the galaxy (38◦). The distinct kinematics of the

red and green GC subpopulations, seen clearly in Fig. 4.11, provide evidence that the

green GCs form a separate subpopulation and are not simply the metal poor tail of the

red subpopulation.

The photometric analysis in Chapter 3 (Blom et al., 2012a) found that the radial

density distribution and colour of the red GC subpopulation most closely matched the

4.4. Discussion 85

stellar light of NGC 4365 and, while the axis ratio of the green subpopulation more closely

matched that of the starlight, they concluded that there was evidence that the red sub-

population was related to the bulk of the galaxy starlight. The analysis here shows that

the rotation position angle, and possibly also the rotation velocity amplitude, of the green

GC subpopulation most closely match the extrapolated stellar kinematics. Thus it is not

immediately clear which of the two is the additional subpopulation.

If the red GC subpopulation is additional it could have been created by an additional,

later (still > 10 Gyr ago) merger (Zepf & Ashman, 1993). This might explain why the

red subpopulation rotation is decoupled from the bulk of the starlight and the green

subpopulation rotates with the bulk of the stars. The problem arises when comparing this

red subpopulation to those of other galaxies of similar luminosity to NGC 4365, as it is

not an outlier but consistent with the colour of red subpopulations of other galaxies of

similar luminosity/mass (Peng et al., 2006).

If the green GC subpopulation has been recently donated from a medium sized galaxy

that accreted on to NGC 4365 then the remnant of this accreted galaxy might still be visible

in the form of a stellar stream across NGC 4365 as reported by Bogdan et al. (2012a).

In Chapter 3 we showed that the green subpopulation is centred at g′ − i′ ∼ 0.97 and we

can estimate the absolute magnitude range for the accreted galaxy by comparing the peak

colour of the green subpopulation with the relationship between peak GC subpopulation

colour and galaxy luminosity empirically determined by Peng et al. (2006). In this case

the green subpopulation of NGC 4365 is the original metal-rich (red) GC subpopulation

of a −14 < MB < −15 dwarf galaxy and we expect to find primarily blue and green GCs

in the stellar stream of NGC 4365. The total stellar light observed in the low surface

brightness stream is B ∼ 13.5 mag (Bogdan et al., 2012a, this a minimum brightness for

the accreted galaxy) corresponding to MB ∼ -18.3 at the distance of NGC 4365. There is a

sizeable mismatch between the predicted absolute magnitude for the dwarf galaxy and the

much brighter integrated stellar light from the stream, suggesting that the stellar stream

is not the remnant of the galaxy associated with accreted green GCs. This explanation has

several other problems. It is still to be explained why the accreted green subpopulation

is so centrally concentrated while the stellar light of the accreted galaxy is still on the

outskirts of NGC 4365. Also why does the green subpopulation rotate with the bulk of

the stellar light of NGC 4365 (along the minor axis of the galaxy) rather than along the

stream orientation, which is roughly aligned with the major axis of NGC 4365?

If the accretion event which brought the green GCs into NGC 4365 was not recent,

the currently observable stellar stream may be unrelated to the green GCs. We can


still investigate this claim based on the number of green GCs (see a similar analysis

by Romanowsky et al., 2012). In Chapter 3 we showed that the green subpopulation

contributes 17 percent of the total GC population or 1100 ± 20 GCs to NGC 4365’s GC

system (NTot = 6450 ± 110). We can calculate the absolute magnitude range of the

accreted galaxy using the observed relationship between GC specific frequency (SN =

NGC100.4(MV +15), Harris & van den Bergh, 1981) and galaxy luminosity (Peng et al.,

2008). To be consistent with the SN range observed for GC systems in the literature, the

accreted galaxy must be brighter than MV ∼ −20.5 (MB ∼ −19.5). The number of GCs in

the green subpopulation far exceeds the expected number of GCs that would be associated

with a dwarf galaxy. If the green subpopulation was donated by an accreted galaxy (at

any stage in the history of NGC 4365) the mean colour of the green GCs suggests a very

small galaxy whereas the number of green GCs suggests a large galaxy.

If the green GCs were accreted from a group of dwarf galaxies it might account for

the large number of GCs with colours that indicate a small host galaxy, but the group of

dwarfs would need to contain many tens of members to account for the large number of

inferred green GCs.

We also consider the possibility that both the red and green subpopulations were

formed in-situ. In this case the green subpopulation would have formed unusually early in

the dense core of the galaxy while the red GCs formed later when the core of the galaxy

was slightly bigger and slightly more metal rich. This scenario might explain why the

green subpopulation is more centrally concentrated than the red subpopulation and why

the green GCs rotate with a significant fraction of the starlight. It does not explain why

we do not detect a fraction of the stars rotating with the red GCs or what would have

caused two epochs of early in-situ GC formation.

Bogdan et al. (2012a) studied the lenticular galaxy NGC 4342 (Vsys = 751 km s−1,

Grogin et al., 1998) embedded in the stellar stream across NGC 4365 (see Fig. 4.11) and

inferred a massive and extended dark matter halo around NGC 4342. The central velocity

dispersions of NGC 4365 (261±7 km s−1) and NGC 4342 (244±11 km s−1) are consistent

within errors (Bernardi et al., 2002) implying similar mass galaxies. It is possible that

this very massive, if relatively faint, galaxy could have passed close by NGC 4365 and

dragged some of NGC 4365’s own stars out from the galaxy to form the stream. It is also

possible that the stream stars have been stripped from NGC 4342 itself. These scenarios

both agree with our observation of low velocity GCs (at ∼ 700 km s−1, similar to the Vsysof NGC 4342) in NGC 4365 (Fig. 4.4) that are aligned with the stellar stream (Figs 4.6

and 4.11). The low velocity GCs are consistent with the colour distribution of NGC 4365’s

4.5. Conclusions 87

underlying GC population (Fig. 4.5) but it is still unknown how their colours compare to

those of NGC 4342.

It is tempting to suggest that the close passage of NGC 4342 might have stretched

NGC 4365 starlight and GC system into its current shape. If NGC 4365 was round or

only mildly elliptical (with a PAphot close to the current PAkin of the bulk of stellar light)

before NGC 4342 made its close pass we would expect to see remnants of the original shape

in the central regions of NGC 4365. Ferrarese et al. (2006) find the galaxy ellipticity drops

below 0.05 in the g and z bands while the PAphot twists to ∼ 90◦ at radii smaller than 0.3

arcsec. These photometric properties only appear at scales smaller than the KDC of NGC

4365 and are unlikely to relate to the bulk of NGC 4365. This scenario might explain the

anomalous ‘rolling’ starlight as an artefact of very recent perturbations to NGC 4365’s

photometric shape but would not explain the presence of NGC 4365’s KDC . The KDC

could be a remnant of an old merger, as its age is observed to be > 10 Gyr and consistent

with the galaxy starlight (Davies et al., 2001) or the consequence of our particular viewing

angle into an intrinsically triaxial system (van den Bergh et al., 1991).

Fig. 4.11 summarises the photometric and kinematic information of the NGC 4365

system graphically. The blue GC subpopulation is the most radially extended GC subpop-

ulation and is more elongated than the galaxy light. Its rotation shows a slow twist from

alignment with the galaxy major axis in the inner parts to the galaxy minor axis in the

outer parts. The amplitude of its rotation and velocity dispersion are constant with radius.

The green subpopulation is the most centrally concentrated, roughly as elongated as the

galaxy light and shows ‘rolling’ kinematics like the galaxy light (PAkin ∼ galaxy minor

axis). Its rotation and velocity dispersion amplitude decrease with increasing radius. The

red subpopulation is almost circular and rotates only in the outer parts, along the galaxy

major axis (in the opposite direction to the KDC). The low velocity GCs are aligned with

the stellar stream and contain GCs of all colour subpopulations. The kinematics of the

galaxy and GC system are complex but it appears that the stellar stream is not connected

to the presence of any one of the GC subpopulations.

4.5 Conclusions

NGC 4365 is a giant elliptical galaxy with rare stellar kinematic properties and also, very

unusually, three globular cluster (GC) subpopulations. Recently, a stellar stream running

across NGC 4365 and its nearby neighbour, NGC 4342, was discovered.

With high velocity resolution Keck/DEIMOS spectra we analyse the kinematics of

a large number and spatially extended sample of GCs around NGC 4365. These data


Figure 4.11: Schematic representation of the NGC 4365 stellar light and GC system.The diagram is oriented with North up and East left. Positions of low velocity GC outliersare shown with black dots. Ellipses show the 2Re extent of the galaxy light (black) andthree GC subpopulations in blue, green and red respectively. The shape and relativesize of the stellar light stream (reported in Bogdan et al., 2012a) is shown with a greyirregular outline. The rotation direction of the galaxy light as well, as the green and redGC subpopulations, are shown in the top right of the diagram. The rotation direction ofthe blue GCs is not shown because there is a significant twist with galactocentric radius.A cross marks the position of NGC 4342. The low velocity GCs are generally aligned withthe stellar stream and have a similar velocity to NGC 4342.

4.5. Conclusions 89

allow us to separate the GC system into its three colour subpopulations and analyse the

kinematics of each subpopulation in detail. We find distinct rotation properties for each

GC subpopulation. This indicates that the three GC subpopulations are distinct and that

the formation history of NGC 4365 is complex. We conclude that it is unlikely that the

third (green) GC subpopulation is related to the existence of the stellar stream and that

it is also unlikely to have originated from an accreted galaxy.

We also find a further group of low velocity GCs (covering the full range in colour),

which might be related to the stellar stream extending across NGC 4365 towards NGC

4342 (∼ 35 arcmin away at a similar systemic velocity to these GCs). The stellar stream

and the low velocity GCs are examined in more detail in Chapter 5.

5Tidal interaction between the early type galaxies

NGC 4365 and NGC 4342

I do not know what I may appear to the world, but to myself I

seem to have been only like a boy playing on the sea-shore, and

diverting myself in now and then finding a smoother pebble or a

prettier shell than ordinary, whilst the great ocean of truth lay all

undiscovered before me.

—Sir Isaac Newton

5.1 Introduction

ΛCDM cosmology predicts that the giant galaxies we see in the local Universe have been

built up to their present size by successive mergers of galaxies and accretion of small galax-

ies. Globular clusters (GCs) are very useful tracers of these merger and accretion events

as they are dense and mostly robust to the violent interactions of galaxies. Additionally,

several extragalactic spectroscopic studies have found that the overwhelming majority of

GCs are > 10 Gyrs old (Strader et al., 2005). Therefore, most of the GCs that we ob-

serve locally were formed very early in the evolution of the Universe and have survived all

the interactions their host galaxies were involved in. They have likely undergone several

galaxy merger events, or have been stripped off smaller galaxies, and accumulated around

large galaxies over time (Cote et al., 1998; Tonini, 2013). They should maintain a chem-

ical signature of the conditions they were formed under and a kinematic signature of the

processes by which they were acquired by large galaxies (West et al., 2004).

NGC 4365 is a giant elliptical galaxy with a redshift independent distance measurement

91

http://en.wikiquote.org/wiki/Isaac_Newton

92 Chapter 5. Tidal interaction between NGC 4365 and NGC 4342

of 23.1 Mpc (Blakeslee et al., 2009) and a recession velocity of 1243 km s−1 (Smith et al.,

2000), placing it ∼ 6 Mpc behind the Virgo Cluster. NGC 4365 is the central galaxy in

the W ′ group and has been noted to have rare properties for a galaxy of its size. It has a

kinematically distinct core and the bulk of its stars rotate along the minor axis (Surma &

Bender, 1995; Davies et al., 2001; Krajnovic et al., 2011, Arnold et al. 2013 in prep.), while

its GC system consists of three subpopulations (Puzia et al., 2002; Larsen et al., 2005;

Brodie et al., 2005; Blom et al., 2012a,b) rather than the usual two (Brodie & Strader,

2006; Peng et al., 2006).

NGC 4342 is a small S0 galaxy 20 arcmin away from NGC 4365 with a recession velocity

of 751 km s−1 (Grogin et al., 1998) but its distance has not been measured independently

of redshift. Recently, Jiang et al. (2012) highlighted it as a local example of a high redshift

superdense ‘red nugget’ (Daddi et al., 2005; van Dokkum et al., 2008) and it has long been

known to have an oversized central supermassive black hole for its bulge mass (Cretton &

van den Bosch, 1999).

In the last year, Bogdan et al. (2012a) presented a very deep B filter image of NGC 4365

showing a stellar stream extending from NE of NGC 4365, across the elliptical galaxy,

bridging the gap between NGC 4365 and NGC 4342, and extending further SW. This

image has prompted investigation of the nature of the interaction between NGC 4365 and

NGC 4342. Bogdan et al. (2012b) found X-ray emitting hot gas around NGC 4342. This

presents an interesting puzzle regarding the origin of the stellar stream. It appears as if

the stellar material has been tidally stripped off NGC 4342 as it passed by NGC 4365

and what remains of NGC 4342 is now swinging back towards NGC 4365. However, the

presence of X-ray emitting gas around NGC 4342 suggests a large dark matter halo that

should not be present if stars have been stripped off the galaxy.

Here we investigate the interaction between NGC 4365 and NGC 4342 using GCs,

assessing what the spatial, colour and velocity distributions of the GCs around NGC 4342

and NGC 4365 indicate about the nature of the interaction between these two galaxies.

In particular, is NGC 4365 stripping stars off NGC 4342 or is there another explanation?

We describe the photometric selection of GC candidates and detail our analysis of the

properties of the spatial and colour distributions of the GC system in Section 5.2. Section

5.3 contains the reduction and analysis of the spectroscopic data as well as the kinematic

selection of GCs. In Section 5.4 we analyse the kinematic properties of the stream and

NGC 4342 GCs and in Section 5.5 we present a counter-argument to the findings of Bogdan

et al. (2012a), before discussing the results and concluding the Chapter in Sections 5.6

and 5.7 respectively.

5.2. Photometric Analysis 93

5.2 Photometric Analysis

5.2.1 Imaging Data

We use the public archival data from the MegaCam instrument on the Canada-France-

Hawaii Telescope (CFHT) to identify GCs in the area around NGC 4342 and NGC 4365.

The square degree u filter image stack is centred on R.A. = 185.912◦ and Decl. = 7.0562◦

(J2000.0) with a total exposure time of 4240 s. The g and i filter image stacks are centred

on R.A. = 186.133◦ and Decl. = 7.2708◦ (J2000.0), and have total exposure times of 3170

and 2055 s respectively. As seen in Figure 5.1, the overlap of the three image stacks covers

an area of ∼ 0.66 square degrees, encompassing NGC 4365, NGC 4342 and almost all of

the stellar stream (Bogdan et al., 2012a). We use catalogues produced by the MegaPipe

data reduction pipeline (Gwyn, 2008), operated by the Canadian Astronomy Data Centre

(CADC), to spatially match point sources found in all three image stacks. The faintest

i magnitude of included objects is 23.0 mag. We correct for foreground reddening, using

Au = 0.11, Ag = 0.08 and Ai = 0.04, determined from Galactic dust maps (Schlegel et al.,

1998).

5.2.2 Globular cluster candidate selection

The selection of GC candidates by comparison with matched point sources is shown in

Figure 5.2. The figure shows (g − i)0 colour plotted against (u− g)0 colour for the point

sources in the field-of-view, as well as the NGC 4365 GC candidates used to identify

the locus of GCs in colour-colour space. These NGC 4365 GC candidates were matched

in spatial coordinates between the MegaPipe catalogues and previous work using the

Subaru/SuprimeCam (SCam) instrument (Blom et al., 2012a). We use the SCam identified

GC candidates to fit a line to the GC distribution, then expand the line by ±2σ to form

a box and select candidates that overlap that box within individual errors. Given the

i = 23.0 mag cutoff, the mean individual errors in the GC candidate sample are ±0.1 mag

in (u − g)0 colour and ±0.04 mag in (g − i)0 colour. As described in Section 2.4.2, the

radial extent of the imaging used affect the distribution of GCs within the selection box

and here, because the both the reference and selection sample are restricted to large radii,

there are more GCs at the blue end of the selection box than at the red end. We identify

a total of 1786 GC candidates that meet these criteria.


Figure 5.1: Spatial alignment of the u ang g filter images from the MegaCam intrumenton the Canada-France-Hawaii Telescope (CFHT). The u filter image is centred on NGC4342 at R.A. = 185.912◦ and Decl. = 7.0562◦ (J2000.0) and shown on the bottom right,below the g filter image, which is centred on NGC 4365 at R.A. = 186.133◦ and Decl.= 7.2708◦ (J2000.0). The i filter image has the same position as the g filter and is notshown here. Each filter image covers one square degree in area and the region of overlapis ∼ 0.66 square degrees.


Figure 5.2: Colour selection of globular cluster (GC) candidates. (g−i)0 colour is plottedagainst (u − g)0 colour. The black crosses show all point sources brighter than i = 21mag and red dots show the objects matched with NGC 4365 GC candidates selected fromSubaru/SuprimeCam photometry (Blom et al., 2012a). The previously photometricallyselected GC candidates form a linear correlation, shown with a black line. We selectadditional GC candidates that are consistent, within individual errors (not shown), withthe red ±2σ box around the line.


Figure 5.3: Spatial distribution of globular cluster (GC) candidates around NGC 4342and NGC 4365. (Left) Individual GC candidates are shown as grey dots and the twogalaxies are shown with circles circumscribing a cross (NGC 4365 towards the top leftand NGC 4342 towards the bottom right). The two-dimensional distribution of the GCcandidates shows a clear overdensity around NGC 4365 and NGC 4342. There are sev-eral galaxies in the region that are as bright as NGC 4342 but none show a similar GCoverdensity. The positions of these galaxies; NGC 4341, NGC 4343, IC 3267 and IC 3259are plotted with x’s. The horizontal void running through the centre of NGC 4365 isdue to CCD chip problem in the u filter. GC candidates are not recovered in the verycentral regions of NGC 4365 and NGC 4342 due to the high galaxy surface brightnessthere. (Right) The two-dimensional spatial distribution of GC candidates smoothed toa resolution of 1.8 arcmin. Here we see an overdensity of GC candidates not only aroundNGC 4342 and NGC 4365 but also between the two galaxies and South West of NGC 4342,that is spatially coincident with the stellar stream. The spatial coincidence of the elon-gated GC overdensity and the recently reported stellar stream (Bogdan et al., 2012a) ishighly unlikely to be a chance occurrence. Both galaxy centres and the horizontal voidare blocked in the smoothed image.


5.2.3 Spatial distribution

Given the selection of GC candidates, Figure 5.3 shows their spatial distribution. The

first panel shows the unsmoothed 2D surface density map. We see a GC overdensity

corresponding to NGC 4365’s GC system and we find an overdensity of GCs around

NGC 4342. The absolute magnitude of NGC 4342 (MB = −18.45 mag for a distance

of 23.1 Mpc) is comparable to that of several other galaxies superimposed on the stellar

stream (i.e. NGC 4341: MB = −17.68, NGC 4343: MB = −19.46, IC 3267: MB = −17.7,

IC 3259: MB = −17.91, assuming the same distance as NGC 4342) however we do not see

a clear overdensity associated with any of them. None of the superimposed galaxies are

likely to be significantly more distant than NGC 4342 (see their quoted recession velocities

in Figure 5.13). We find that NGC 4342 has an order of magnitude more GCs than the

nearby galaxies of similar luminosity, indicating that it has deviated significantly from a

‘normal’ evolutionary history.

The 2D distribution of GC candidates is smoothed to a spatial resolution of 1.8 arcmin

in the second panel of Figure 5.3. Once the data are smoothed the GC overdensity along

the stellar stream is also apparent. The GC overdensity bridges the gap between NGC 4365

and NGC 4342 and extends further SW of NGC 4342, in roughly the same line as the

bridge part of the GC stream. This result is robust to small variations in the magnitude

cut and the spatial resolution of the smoothing. The number of GCs contributing to

the visible stream overdensities is on the order of tens of GCs and it is conceivable that

such a stream structure could arise by chance. We see a possible example of this chance

structure extending NW from NGC 4365. This possibility is testable if the distribution

of GCs expected from both NGC 4365 and NGC 4342 were simulated and compared with

the actual spatial distribution. In order to simulate the spatial distribution of the GC

contribution from these two galaxies, their radial and azimuthal distributions need to be

determined.

Spatial properties of NGC 4342’s GC system

With∼ 140 photometrically observed GC candidates in the spatial overdensity seen around

NGC 4342 in the left panel of Figure 5.3 we can determine some of the quantitative param-

eters of the GC distribution. The radial surface density of GC candidates, shown in Figure

5.4, is calculated in annuli around NGC 4342. No GC candidates are detected within 0.5

arcmin from the centre of NGC 4342 because the high starlight surface brightness there

inhibits object detection. Consequently the first radial bin (between 0.5 and 1.5 arcmin)

is possibly also affected by this incompleteness. To ensure that there are significant GC


Figure 5.4: Radial surface density of GCs brighter than i = 23 mag around NGC 4342plotted against galactocentric radius. The dots with error bars show the surface densitypoints and the best fit Sersic profile (with an additional background term) is plotted witha solid line. The ranges of Sersic parameters that would still fit the data points withinerrors are also shown. The two dotted lines forming an envelope to the best fit line showthe minimum and maximum range for the fitted parameters (Pe, Re, n) respectively. Thethird dotted line shows a Sersic profile where the shape parameter n is 2.3 times the bestfit value, consistent with all the data points. The data are well matched by the Sersicprofile fit but without data points closer than 1 arcmin from the galaxy centre it is notpossible to constrain the fit well. See text for further details.


candidate numbers in each bin we divide the data into only five annuli and fit a Sersic

profile added to a background term to these. The Sersic profile and background term (bg)

is given by (Graham & Driver, 2005):

P (R) = Pe exp

(−bn

[(R

Re

) 1n

− 1

])+ bg (5.1)

where

bn = 1.9992n− 0.3271 (5.2)

Pe is the density at the effective radius, Re is the effective radius of the GC system,

and n is the shape parameter of the Sersic profile. The best fit profile is given by:

Pe = 1.68± 0.19 arcmin−2

Re = 2.12± 0.12 arcmin

n = 0.50± 0.12

bg = 0.782± 0.025 arcmin−2

The value of the background term and the effective radius of the GC system are well

constrained by the five surface density values obtainable from the MegaCam data set.

Here the value for the background surface density will include non-GC contamination and

GCs that are spatially associated with the stellar stream rather than NGC 4342 itself. We

measure the effective radius of NGC 4342’s GC system to be 2.12 ± 0.12 arcmin (∼ 14

kpc) compared with the stellar effective radius of 0.5 arcmin (∼ 3 kpc) (de Vaucouleurs

et al., 1991). The best fit profile is plotted on Figure 5.4. The truncation of NGC 4342

GCs at ∼ 5 arcmin is secure and we can assert that the bound GC system of NGC 4342

does not extend beyond ∼ 34kpc. It is unusual for a GC system to have a value < 1 for

the shape parameter n (Rhode & Zepf, 2004; Spitler et al., 2006a, 2008; Pota et al., 2013)

and we also plot a Sersic profile where n = 1.15, as we suspect that the fitted error for

n is underestimated due to the small dataset. The data cannot rule out this (n = 1.15)

fit as it is consistent within errors with every data point. To improve the constraint on

the shape parameter n, more GC data are needed in the central regions of the galaxy.

To probe further into the centre of NGC 4342 better imaging and careful subtraction of

the galaxy light is required. If n is indeed < 1, the GC system of NGC 4342 is sharply

truncated and could imply that it has been tidally stripped.

The azimuthal distribution of GCs around NGC 4342 is consistent with a circular GC


Figure 5.5: Azimuthal distribution of GCs within 5 arcmin from the centre of NGC 4342and brighter than i = 23 mag. This distribution indicates that the GC system of NGC 4342is consistent with zero ellipticity (a circular GC distribution). If the GC distributionwas significantly elliptical the azimuthal distribution would show a clear sinusoidal shapewith the peak of the distribution at the position angle of the elliptical distribution. Thephotometric position angle of NGC 4342 is 166◦.

system (see Figure 5.5). Even with azimuthal bins encompassing 18 degrees in radius,

the distribution of GC candidates does not show a signficant peak at any one position

angle. It is possible that deeper imaging, probing fainter GCs and closer to the galaxy

centre would show a small but significant ellipticity. The photometric major axis position

angle of NGC 4342 is 166◦ and it would be interesting to determine if the GC distribution

position angle is consistent with that of the galaxy or the stellar stream (∼ 45◦).

Statistical significance of GC overdensity on the stream

We generated 1000 simulated spatial distributions around NGC 4365 and NGC 4342 of

which Figure 5.6 is an example. These distributions are the addition of a uniform back-

ground sampling added to a sampling from the spatial distributions of both NGC 4365

and NGC 4342. Each simulated spatial distribution contains 2000 objects. This number

was chosen to be higher than the number of objects in the observed distribution (1786)

because there are voids in the observed spatial distribution. The background was set at a


density of 0.5 arcmin−2 across the entire area. This value was chosen to visually match the

object density far from NGC 4365, NGC 4342 and the stream on the CFHT/MegaCam

observations. The background value of 0.78 measured in Section 5.2.3 is likely to include

stream GCs as well as non-GC contaminating objects. The rejection method was used

to extract random positions according to the spatial distribution functions. We used the

Sersic profile and azimuthal distribution fitted by Blom et al. (2012a) to simulate the

radial and azimuthal distribution of NGC 4365 and the previously fitted Sersic profile

for NGC 4342. The Sersic profile was noted to be highly uncertain in the inner regions

of NGC 4342, but the fitted profile is sufficient for these purposes as we are primarily

concerned with the outer regions of both galaxies’ spatial distributions. Figure 5.6 also

shows the specific areas which were compared to the observed spatial distribution.

The overdensity in the two stream areas was calculated by dividing the density in the

stream defined areas by the density in the off-stream areas alongside (see Figure 5.6). For

the observed spatial distribution we calculated a stream overdensity of 1.66 for the stream

area between NGC 4365 and NGC 4342 and an overdensity of 1.90 for the stream area SW

of NGC 4342. These observed stream overdensities are compared with the distribution of

stream overdensities in Figure 5.7. An overdensity equal to or greater than the observed

value on the stream between NGC 4365 and NGC 4342 occurs by chance 6.9 percent of

the time but only 2.3 percent of the time on the stream beyond NGC 4342.

An overdensity of GCs between NGC 4365 and NGC 4342 could be due to the effects

of smoothing large and overlapping GC systems from both galaxies, but we also see a clear

overdensity of GCs along the stellar stream South West of NGC 4342. The extension of

the GC distribution is highly unlikely to occur by chance and is best explained by tidal

stripping of GCs from one galaxy onto another.

5.2.4 Colour distribution

We have calculated the transformation from (g′ − i′)0 in the SDSS photometric system

(used for the SCam photometry) to (g − i)0 in the MegaCam photometric system. The

data used for the calculation and the resulting transformation are shown in Figure 5.8.

We find a shift of −0.102 mag from the SDSS to MegaCam colours, that is not strongly

dependent on (g − i)0 colour over the colour range of GC candidates.

Comparing the colour distributions of GC candidates associated with NGC 4365,

NGC 4342 and the stellar stream (see Figure 5.9) we see a significant population of red

(metal-rich) GCs present in NGC 4365’s GC system that is absent from the colour distri-

bution of either the stream or NGC 4342. There is no significant difference between the


Figure 5.6: An example of the 1000 simulations of the spatial distribution of the GCsaround NGC 4365 and NGC 4342. Each simulated spatial distribution contains 2000GC candidates, created by the addition of a uniform background, the Sersic profile ofNGC 4342 GCs and the spatial distribution of NGC 4365 GCs. NGC 4365’s GC spatialdistribution includes the Sersic profile and azimuthal distribution fitted in Blom et al.(2012a). The solid orange and magenta regions mark the areas of stream overdensitybetween the two galaxies and SW of NGC 4342 respectively. These stream overdensitiescan be seen in Figure 5.3. The dotted orange and magenta off-stream regions on eitherside of the solid boxes are used to compare off-stream densities.


Figure 5.7: Cumulative distribution of the calculated stream overdensity for 1000 sim-ulated GC spatial distributions. The dashed orange and solid magenta lines show theon-stream density divided by the off-stream density for the stream region between the twogalaxies and SW of NGC 4342 respectively. The dash-dotted orange and dotted magentalines show the overdensity measured in the same stream regions for the observed GC can-didate spatial distribution. The horizontal black dashed line shows the 95th percentile.The orange distribution shows that the region between NGC 4365 and NGC 4342 is morelikely to show an overdensity due to the significant ellipticity and stream aligned positionangle of NGC 4365. The intersection of the orange dash-dotted and dotted lines falls be-low the 95th percentile but the intersection of the magenta dotted and solid lines falls wellabove the 95th percentile. The GC stream overdensity SW of NGC 4342 is a statisticallysignificant deviation from the GC distributions of NGC 4342 and NGC 4365.


Figure 5.8: Scaling between Subaru/SuprimeCam (g′−i′)0 and CFHT/MegaCam (g−i)0colours. Points with error bars are the objects with photometry from both SCam andMegaCam and MegaCam i magnitude brighter than 23 mag. The black line shows a one-to-one correlation between the (g − i)0 colours. The SCam colours have been shifted by−0.102 mag and the comparison between shifted SCam and MegaCam colours is consistentwith a one-to-one correlation over the GC colour range.


Figure 5.9: Histogram of (g − i)0 GC colours for NGC 4365, NGC 4342 and the stellarstream. The colour distribution of GCs within 5 arcmin of NGC 4342 is plotted witha dashed line and the colour distribution of GCs that overlap with the stream regionsas defined in Figures 5.3 and 5.6 is plotted with a dotted line. The colour distributionof NGC 4365 is plotted with a solid line. It has been shifted from Subaru/SuprimeCam(g′ − i′)0 (Blom et al., 2012a) to CFHT/MegaCam (g − i)0 colours and scaled down forcomparison. The colour distribution of NGC 4342’s GC system is indistinguishable fromthe colour distribution of the GC stream and both lack the red GCs seen in the colourdistribution of NGC 4365’s GC system.


GC colour distribution of the stream population and that of NGC 4342. This supports

the theory that the stream GCs are actually GCs from the outer parts of NGC 4342’s GC

system that have been stripped off the galaxy during a tidal interaction with NGC 4365.

5.3 Spectroscopic Analysis

5.3.1 Spectroscopic Data

Three slitmasks were observed on 2012, April 17 with the DEep Imaging Multi-Object

Spectrograph (DEIMOS) on the Keck II telescope. The slitmasks were positioned along

the stream South West of NGC 4365 and around NGC 4342. GC candidates within each

slitmask were chosen for observation based on solely their brightness. The slitmasks were

each observed for a total of 2 hours, split over 4 observations of 30 min each. The median

seeing was 1.2 arcseconds. For all 3 masks, the slits were 1 arcsecond wide and a 1200 l/mm

grating, centred on 7800 A, was used in conjunction with the OG550 filter. This setup

(Romanowsky et al., 2009; Arnold et al., 2011; Foster et al., 2011; Strader et al., 2011;

Romanowsky et al., 2012) enables observations from ∼ 6550− 8900 A with a wavelength

resolution of ∼ 1.5 A. The Calcium II triplet (CaT) absorption features (8498, 8542 and

8662 A) are within the observed wavelength range up to recession velocities of ∼ 8 200

km s−1. This includes NGC 4342 (V = 751 km s−1, Grogin, Geller & Huchra, 1998) and

NGC 4365 (V= 1243 km s−1 Smith et al., 2000). We use these absorption features to

determine the line-of-sight velocity for each candidate GC.

The data were reduced using a modified version of the deep2 (Deep Extragalactic

Evolutionary Probe 2) galaxy survey data reduction pipeline (idl spec2d Cooper et al.,

2012; Newman et al., 2012). The pipeline uses dome flats, NeArKrXe arc lamp spectra and

sky light visible in each slit to perform flat fielding, wavelength calibration and local sky

subtraction, respectively. Newman et al. (2012) state that the deep2 pipeline achieves

a root-mean-squared (rms) accuracy of 0.007A, with adequate arclamp exposure and a

fifth order Legendre polynomial iterative fit. This rms is determined from the final mean

residual about the fit. The pipeline outputs sky subtracted object spectra as well as the

sky spectra themselves.

Each object spectrum was cross correlated (using the iraf task rv.fxcor) with the

CaT features in 13 stellar template spectra observed with the same setup. The velocity

of the object is calculated by taking the mean of the 13 cross correlation values and the

error on the velocity is calculated by adding in quadrature the standard deviation of the

velocities and the mean of the errors on those velocities (as determined by rv.fxcor).

5.3. Spectroscopic Analysis 107

Figure 5.10: Comparison of independent velocity measurements from DEIMOS spectra.Velocities are plotted with error bars on a one-to-one relationship line. All the velocitiesare consistent with each other except the lowest velocity, at −200km s−1 as identified bythe second measurement. We exclude this object from further analysis and measure anrms dispersion about the one-to-one line of 8.8 km s−1.


The process of velocity determination was independently repeated by a collaborator (C.

Foster) and results are shown in Figure 5.10. We only include a velocity measurement

in further analysis, if it has a reliable velocity (as determined by both investigators) and

the measured velocities are consistent within the measured errors (i.e. the 1σ error bar

of one measurement overlaps the other velocity measurement). The root-mean-squared

dispersion between the two independent measurements is 8.8 km s−1. The rms dispersion

between the independent measurements of recession velocity result from the inclusion of

a few reliable, but lower signal-to-noise spectra.

5.3.2 Velocity selection

If the GC candidates on the stream and around NGC 4342 originate from the same system,

we expect their recession velocities to be consistent. A superposition of NGC 4342 GCs

on a population of stream GCs that have been stripped from another, now completely

disrupted galaxy, might show an offset in recession velocity.

We find 33 objects with measurable recession velocities between -200 and 1600 km s−1

but assume the 11 objects with velocities smaller than 400 km s−1 to be stars in our own

Galaxy. The remaining 22 GCs are plotted in Figure 5.11 showing GC recession velocity

against galactocentric radius from NGC 4365. The new velocities presented in this work

scatter about the recession velocity of NGC 4342 (V = 751 km s−1). The velocities of GCs

around NGC 4365 are also shown along with the 2σ dispersion envelopes as a function of

galactocentric radius. As highlighted in Blom et al. (2012b), there is a grouping of low

velocity GCs that are also consistent with NGC 4342’s recession velocity.

Four of the 22 GCs with measured velocities are more likely to be associated with

NGC 4365 than NGC 4342 and the stream. Two have significantly higher velocities than

NGC 4342 (> 1400 km s−1) and another two have reasonably high velocities (> 1050 km

s−1); all are radially consistent with NGC 4365’s GC system. We associate 18 new GC

recession velocities with the sample of 13 stream GCs identified in Blom et al. (2012b)

giving a total of 31 GCs associated with NGC 4342 and the stream. The positions,

recession velocities and photometric colours of these GCs are listed in Table 5.1. We

conclude that GCs associated with NGC 4342 and the stream are indistinguishable in

velocity.

5.3. Spectroscopic Analysis 109

Figure 5.11: Phase-space diagram of globular cluster line-of-sight velocity as a functionof distance from NGC 4365. New GC velocities presented here are shown as triangles withblack outlines and error bars while previously published GC velocities (Blom et al., 2012b)are shown without. The colour of each symbol indicates its (g − i)0 colour. NGC 4365and NGC 4342 are marked with crosses at 0 arcmin, V = 1243 km s−1 and ∼20 arcmin,V = 751 km s−1 respectively. The solid line shows the 2σ envelopes of the GC velocitydistribution and the dashed line indicates the adopted boundary, below which we classifyobjects as low velocity stream GCs. The 31 stream and NGC 4342 GCs have a similarmean velocity to NGC 4342. The (g − i)0 colours of low velocity GCs are mostly bluewith some green GCs but no red GCs. Red GCs are only found in the central regions ofNGC 4365. The stream GCs are likely tidally stripped from the original GC system ofNGC 4342.


I.D. R.A. Decl. V Verr (g − i)0(degrees) (km s−1) (mag)

1 185.9401 7.031459 1060.96 32.67 0.7392 185.9213 7.035125 863.96 24.33 0.6663 185.9598 7.045965 706.69 16.77 0.8464 185.9558 7.045967 831.14 14.73 0.7085 185.9771 7.048600 758.41 22.65 0.9116 185.9753 7.082502 795.31 17.96 0.8617 185.9512 7.099172 897.06 17.68 0.7868 186.0573 7.182025 627.83 35.51 0.8549 185.7894 6.944254 870.57 20.42 0. 67410 185.8825 6.988903 811.86 34.09 0.95311 185.8480 7.014668 769.10 41.91 0.74112 185.9185 7.019106 770.51 19.25 0.77313 185.8866 7.040393 661.51 32.29 0.72714 185.9483 7.063918 778.85 25.04 0.81415 185.7881 7.030769 724.31 29.35 0.69616 185.8933 7.083244 1008.32 16.11 0.74817 185.8355 7.081957 460.64 33.42 0.77518 185.8873 7.147331 664.27 39.24 0.75819 186.0902 7.243450 796.64 11.37 0.92520 186.1140 7.328690 538.85 7.39 0.98421 186.0164 7.152190 519.93 9.97 0.87022 186.0573 7.182080 624.60 6.26 0.65923 186.0640 7.224360 734.53 5.15 0.76024 186.1108 7.315610 698.58 15.57 0.92325 186.1011 7.325980 712.22 12.39 0.84526 186.1203 7.300140 720.09 8.61 0.71227 186.1341 7.368620 682.49 14.93 1.07728 186.0563 7.251730 758.64 12.42 0.64229 186.1403 7.175740 668.30 4.22 0.80830 186.1637 7.145680 797.62 5.46 0.68031 186.0988 7.293170 673.24 4.55 0.877

Table 5.1: Positions, recession velocities and photometric colours (CFHT/MegaCam)for the 31 GCs associated with NGC 4342 and the stream. GCs numbered 1 to 18 arepresented for the first time in this work and GCs numbered 19 to 31 are low velocityoutliers previously presented in Blom et al. (2012b).

5.4. Kinematic properties of NGC 4342’s GC system 111

5.4 Kinematic properties of NGC 4342’s GC system

Taking the stream GCs as part of the GC system of NGC 4342 we calculate a mean

velocity of 742 ± 23 km s−1 and a velocity dispersion of 127 ± 16 km s−1 (31 GCs). If

GCs are being stripped off NGC 4342 to form the GC stream we expect the GCs closest

to NGC 4365 may not be bound to NGC 4342. Therefore in Figure 5.12 the GCs are

divided into three radial bins: the outer bin for GCs that are less than 15 arcmin from

the centre of NGC 4365 and the inner two bins for GCs within 3 arcmin and 10 arcmin

of NGC 4342 respectively. The 8 GCs within 3 arcmin from NGC 4342 have a mean

projected distance of 2.14 arcmin, a mean velocity of 835 ± 49 km s−1 and a velocity

dispersion of 139± 37 km s−1. The 9 GCs between 3 and 10 arcmin from NGC 4342 have

a mean projected distance of 4.2 arcmin, a mean velocity of 750±43 km s−1 and a velocity

dispersion of 130±32 km s−1. The 14 GCs within 15 arcmin from NGC 4365 have a mean

projected distance from NGC 4343 of 14.9 arcmin, a mean velocity of 682±22 km s−1 and

a velocity dispersion of 83± 16 km s−1. There is a significant decrease in mean recession

velocity (from 835 to 750 to 682 km s−1) with increasing distance from NGC 4342, but

it is not possible to disentangle a possible velocity shear along the GC stream from the

effect of one-sided selection in velocity space as NGC 4365’s GC system overlaps the GCs

along the stream. The outermost velocity dispersion point in Figure 5.12 is also probably

artificially decreased because the velocities overlap with NGC 4365’s GCs and the GCs

there are unlikely to be bound to NGC 4342. We do not see a significant decrease in the

measured velocity dispersion at 4.2 arcmin (3 to 10 arcmin from NGC 4342) compared to

that at 2.14 arcmin (within 3 arcmin from NGC 4342). If the mean GC velocity is fixed

to the recession velocity of NGC 4342 (751 km s−1) the calculated velocity dispersion

increases to 165±44 and 110±22 km s−1 for the innermost (mean distance at 4.2 arcmin)

and outermost bins respectively (mean distance at 14.9 arcmin). If three GCs with 2σ

velocities are excluded from the sample, the calculated velocity dispersion for the inner

bins decrease to ∼ 75± 23 km s−1.

In Figure 5.13 the 31 velocity selected NGC 4342/stream GCs are plotted on an image

of the stellar stream. The recession velocities of the GCs and nearby galaxies are marked

by the colour of the points (i.e. NGC 4341: V = 922 km s−1, NGC 4343: V = 1014 km

s−1, IC 3267: V = 1231 km s−1, IC 3259: V = 1406 km s−1). We note that NGC 4342 is

the only galaxy in the area that has a low enough recession velocity to be consistent with

the recession velocities of the GCs around it and extending along the stream. Given that

the GCs in the area are moving with NGC 4342 and are strongly spatially aligned with

the stream we conclude that the GC system of NGC 4342 is being stripped through tidal


Figure 5.12: Velocity dispersion of globular clusters associated with NGC 4342 andthe stream are plotted with filled points as a function of the distance from the centre ofNGC 4342. Errorbars in the x-direction show the radial extent of the bins. The grey regionshows the velocity dispersion range as calculated for the entire sample of 31 GCs, i.e. 111to 143 km s−1. The outermost filled point shows a velocity dispersion for GCs that areunlikely to be bound to NGC 4342. The open symbols with errorbars show the velocitydispersion calculated when three GC velocities are excluded from the sample. These GCsfall between 2.1 and 2.5 σ from the mean velocity of NGC 4342’s GC system.

5.5. Dark matter constraints 113

Figure 5.13: Imaging of the giant elliptical NGC 4365 and other nearby galaxies in theW ′ group. A very deep image which reveals a∼ 300 kpc long stream of stars (Bogdan et al.,2012a). Globular clusters (GCs) in the stream are represented by triangles and galaxiesby circles, with the colour of the points denoting their recession velocity (velocities > 1900km s−1 are assigned the reddest colour). The GCs have a mean velocity consistent withthe recession velocity of NGC 4342 (located at R.A. ∼ 185.9◦ and Dec. ∼ 7.05◦) indicatingthat they were tidally stripped along with some stars from NGC 4342. The other galaxiesin the W ′ group have recession velocities intermediate between those of NGC 4342 andNGC 4365.

interaction with NGC 4365. This implies that dark matter is being stripped form NGC

4342 as well.

5.5 Dark matter constraints

This Section, including Figure 5.14, was prepared by Dr. Aaron J. Romanowsky, a co-

author on Blom et al. (2013), and is included in this Thesis for continuity of argument.

We now consider the main challenge to the tidal stripping scenario raised by Bogdan

et al. (2012a), which is the dark matter (DM) halo of NGC 4342. Because the DM would

have much lower initial binding energies on average than the stars, it should be stripped


first, and severe stripping of the stars should not begin until a large fraction of the DM

is stripped away. Thus at first glance, the inference from the X-ray gas of a massive DM

halo would seem to rule out the tidal scenario. However, there are still two key points to

consider: (1) quantifying the degree of possible stripping; (2) more critically examining

the X-ray mass inference itself, given the strong disturbances observed in the gas.

The first point is especially relevant in the light of a new determination that the tidal

stripping may have been at only the ∼ 50 percent level (Forbes et al. 2013) rather than

the & 90 percent level adopted by Bogdan et al. (2012a). There would still be a non-

zero amount of DM remaining, and it is not immediately obvious how much to expect.

Indeed, simulations of the tidal stripping of satellite galaxies have predicted surprisingly

resilient DM halos. In one case, Goerdt et al. (2008) examined small disk galaxies with

ΛCDM-like halos within a Virgo cluster-like potential, and concluded that “Even when

the entire [baryonic] disk is tidally stripped away, the nucleus stays intact and can remain

dark matter dominated even after severe stripping.” Libeskind et al. (2011) simulated

the mass stripped from small galaxies (subhalos) located within the larger DM halo of a

massive galaxy. They found after a billion years, tidal stripping had indeed removed most

of the DM, but 30 percent remained along with 60 percent of the original stellar mass (i.e.

40 percent of the stellar mass was stripped away).

These works suggest that it is plausible for a significant fraction of DM to remain

around NGC 4342 even after strong stripping. Investigating the question further would

require a simulation tailored to the NGC 4342 and NGC 4365 system, which is beyond

the scope of this work. However, we can at least compare the DM profile inferred from

the X-rays to a standard ΛCDM halo and see if there is any evidence of deviation from

the norm as may be expected after stripping. We therefore construct a simplified mass

model for NGC 4342 that consists of a stellar component plus a DM halo with a profile

predicted by ΛCDM simulations (Navarro, Frenk & White, 1997). The stellar mass-to-

light ratio is estimated based on the B − V = 0.96 colour to be M/LB ' 4.2 (Bell et al.

2003, for a Chabrier 2003 initial mass function), which translates to a stellar mass of

M? ' 1.6 × 1010M�. From Dutton et al. (2010) we expect a corresponding virial mass

of Mvir ' 1.2× 1012M�, and from Prada et al. (2012) we predict a halo concentration of

cvir ' 10.7.

The predicted circular velocity profile is plotted in Figure 5.14 (“mini halo”); one

expects that outside the very central regions, the profile will be fairly constant with a

circular velocity of vc ∼ 140–150 km s−1. We also plot the X-ray result, showing the

full range of reported uncertainties from different azimuthal sectors in order to allow

5.5. Dark matter constraints 115

Figure 5.14: Circular velocity profile of NGC 4342. Predictions are shown based on thestellar mass only (dashed curve), a standard dark matter halo for a small galaxy (solidcurve), or for a large galaxy (dotted curve). The results based on X-rays are shown as asolid curve with shaded uncertainty region, and are in strong conflict with any reasonableΛCDM-based halo. This Figure was prepared by Dr. Aaron J. Romanowsky, a co-authoron Blom et al. (2013), and is included in this Thesis for continuity of argument.

conservatively for systematic uncertainties from the ram pressure effects. The X-ray profile

is dramatically different from the ΛCDM-based prediction, implying vc ∼ 260–300 km s−1,

which brings us back to our second question above, about the reliability of the X-ray

constraints.

We next consider a much more massive DM halo, corresponding to an ∼ L∗ elliptical

galaxy, with Mvir = 6.0×1012M� and cvir = 9.5. As shown in Figure 5.14 (“big halo”), in

this case the vc profile rises to ∼ 200 km s−1 at ∼ 20 kpc, but still conflicts with the X-ray

result. A halo that matches the X-ray profile requires an extremely high concentration,

e.g., cvir ∼ 40 for Mvir ∼ 3× 1012M�, which would make it an outlier at a ∼ 5σ level from

the expected mean ΛCDM relation.

One point to note here is that Bogdan et al. (2012a) considered the assumption of hy-

drostatic equilibrium to be questionable beyond ∼ 5 kpc, and relied on information within

only ∼ 10 kpc. By adding some combination of adiabatic halo contraction (Blumenthal


et al., 1986) and a higher stellar mass-to-light ratio, the model predictions (for both mini

and big halos) would be in better agreement with the X-rays inside ∼ 5 kpc, while also

perhaps being more consistent with the central stellar kinematics from (Cretton & van den

Bosch, 1999). However, this also implies that in these central regions, the X-rays cannot

really discriminate between massive and stripped halos, and that at larger radii, they may

be completely unreliable (note that a similar problem was also seen in another galaxy by

Romanowsky et al. 2009). Additional information about the halo mass could in principle

come from the dynamics of the GCs around NGC 4342. We have explored this possibility

using spherical, anisotropic Jeans models, and comparing to the velocity dispersion data.

Unfortunately, we found that the results are very sensitive to the inclusion or exclusion of

a few velocities.

5.6 Discussion

The properties of the GC system around NGC 4342 indicate that the galaxy is being

tidally stripped. We find a distribution of GCs peaked at the position of NGC 4342.

Detailed analysis of the GC spatial distribution reveals a statistically significant GC stream

overlapping with NGC 4365’s GC system (NE of NGC 4342) and extending a roughly equal

distance SW of NGC 4342. The (g− i)0 colour distribution of the GCs close to NGC 4342

is indistinguishable from that of GCs in the stream and significantly different from that of

NGC 4365’s GC colour distribution. This suggests that the stream GCs have been drawn

from the outskirts of NGC 4342’s GC system. In addition to the similarity of their spatial

and colour properties, the recession velocities of stream and NGC 4342 GCs (V = 742±23

km s−1) match the recession velocity measured from NGC 4342 starlight (V = 751 km

s−1) very well.

At first glance the evidence from stellar light also supports this interpretation. Bogdan

et al. (2012a) report the presence of a stellar stream extending from the NE corner of

NGC 4365 across the galaxy, through NGC 4342 and further SW, which aligns well with

the GC stream. As NGC 4342 is a much less luminous galaxy (MB = −18.45 mag) than

NGC 4365 (MB = −21.3 mag) it seems reasonable to assume that NGC 4365 is tidally

stripping NGC 4342 as it moves through the W ′ group. Closer investigation of NGC 4342’s

properties brings this into question as Bogdan et al. (2012b) find X-ray evidence of hot

gas surrounding NGC 4342 and infer a large dark matter halo.

If NGC 4342 still has a large dark matter halo it becomes very difficult to explain

how a large percentage of stars and GCs have been stripped off the galaxy during a

tidal interaction without stripping an even larger fraction of the dark matter (Libeskind

5.6. Discussion 117

et al., 2011). If the majority of the dark matter and GCs as well as 50 percent of the

stars have been stripped off NGC 4342 (see Forbes et al. 2013), the original dark matter

halo of NGC 4342 must have been very large and that dark matter is unlikely to have

escaped the potential well of NGC 4365 at the centre of the W ′ group. If the stars have

instead been stripped off NGC 4365 and are in the process of accreting onto NGC 4342

we expect GCs from the outskirts of NGC 4365 to be accreting onto NGC 4342 as well.

Given the measured recession velocities of NGC 4365, NGC 4342 and the stream GCs,

this interpretation of the stream GCs originally coming from NGC 4365 is highly unlikely.

It is possible that measurements of the X-ray halo do not imply as large a dark matter

halo as first thought. Bogdan et al. (2012b) noted that the X-ray emitting hot gas is

sharply truncated on the NW edge of NGC 4342 due to ram-pressure as it moves towards

NGC 4365 at 1300 km s−1 at an angle of 22◦ with respect to the viewing plane. This

hydrodynamic disturbance is likely to affect the dark matter inference beyond 5 kpc from

NGC 4342. We showed in Figure 5.14 that inside 5 kpc the X-ray measurement cannot

distinguish between a galaxy with a very large dark matter halo or one with a moderate

amount or even no dark matter. With a moderate, or very small, dark matter halo there

are no reasons why NGC 4342 could not have been partially stripped of its GCs and stars.

Further support for the tidal stripping scenario comes from investigation of NGC 4342

in the context of galaxy scaling relations and comparison to well know tidally stripped

galaxies (Forbes et al. 2013).

We also consider a third galaxy with the same recession velocity and GC system

properties as NGC 4342. If interaction with NGC 4342 has completely disrupted this

galaxy and both its stars and GCs are now being accreted onto NGC 4342, there is no

need for stars to be stripped from NGC 4342 before its dark matter halo. It is not far-

fetched to assume a galaxy with an almost identical GC colour distribution to NGC 4342

as many galaxies have GC systems with similar colour distributions (Peng et al., 2006), but

it might be somewhat more problematic to also assume that such a galaxy had exactly the

same recession velocity as NGC 4342. However, many of the galaxies around NGC 4342

have similar recession velocities and recent observations have shown that small galaxies

accreting along streams or filaments might not be as uncommon as previously thought

(Ibata et al., 2013). Nevertheless, we consider this possibility as less likely than NGC

4342 itself simply being stripped.


5.7 Conclusions

We find an overdensity of globular clusters (GCs) centred on the S0 galaxy NGC 4342

and extending into a statistically significant GC stream (at the 97.7 percent level) that

is spatially coincident with the stellar stream crossing NGC 4365, as reported by Bogdan

et al. (2012a). The photometric colours of the stream GCs match the colours of the GCs

around NGC 4342 and their measured recession velocities (V = 742 ± 23 km s−1) match

the measured recession velocity of NGC 4342 itself (V = 751 km s−1). The evidence from

these observations indicate that stars and GCs from NGC 4342 are being tidally stripped

by the larger galaxy NGC 4365, situated at the centre of the W ′ group.

This finding apparently contradicts the conclusion that NGC 4342’s stars and GCs

have not have been stripped because it hosts a large dark matter halo that has not been

stripped (Bogdan et al., 2012b). We found that where the dark matter halo inference is

secure (within 5 kpc from NGC 4342 where it is not hydrodynamically disturbed) it cannot

differentiate between a large dark matter halo or none at all. Therefore, it is possible that

NGC 4342 stars and GCs are being accreted by NGC 4365. After considering a third

galaxy, with the same recession velocity and GC colour as NGC 4342, that has been

disrupted to form the stream we conclude that NGC 4342 is most likely being tidally

stripped to form the stellar and GC stream.

6Conclusions and future work

Science always doesn’t go forwards. Its a bit like doing a Rubik’s

Cube, you sometimes have to make more of a mess with a Rubik’s

Cube before you can get it to go right.

—Dame Jocelyn Bell Burnell

Each individual Chapter contains a discussion of the results presented therein and

highlights the implications for theoretical scenarios of galaxy evolution. Therefore, we will

present a summary of the results and conclusions from the individual chapters here, before

continuing to describe potential avenues for further research. We conclude this thesis with

commentary on how the research presented here contributes to current studies of galaxy

evolution.

6.1 Chapter Summary

In Chapter Two we presented a procedure for finding globular clusters (GCs) around

the giant elliptical galaxy NGC 4365. We contrasted the techniques of finding GCs with

space based imaging (where GCs are spatially resolved but the field of view of each image

is small) with ground based imaging (where GCs are point sources, indistinguishable in

angular size from stars but the instrument field of view can be much larger).

The photometric properties of NGC 4365’s GC system were analysed in Chapter Three

of this thesis. One of the main aims of this Chapter was to determine if the previously

identified third GC colour subpopulation (Puzia et al., 2002; Brodie et al., 2005; Larsen

et al., 2005) is definitively separate from the usual two GC subpopulations. The analysis

was done on a combination of one wide-field, three filter Subaru/Suprime-Cam image

and eight, two filter Hubble Space Telescope/Advanced Camera for Surveys pointings.

119

http://en.wikipedia.org/wiki/Jocelyn_Bell_Burnell

120 Chapter 6. Conclusions and future work

We found a significant gradient of mean colour with GC magnitude for the blue GC

subpopulation and a significant gradient in mean colour with galactocentric radius for

both blue and red GC subpopulations. Neither the colour-magnitude (blue-tilt) or colour-

radius relations are strong enough to explain the overdensity of intermediate colour GCs

at small galactocentric radii. Therefore GC system of NGC 4365 was divided into three

GC subpopulations: the blue, green (intermediate colour) and red subpopulations. During

our investigation of the properties of each subpopulation we found:

• The radial surface density of the blue GC subpopulation is clearly distinct from that

of the red subpopulation and the green subpopulation radial surface density is only

significantly different to that of the red subpopulation in the inner 1.2 arcmin (2

data points).

• The median half light radius (mean size) of the blue GC subpopulation is more than

3σ different from the green and read GC subpopulation sizes. The difference in

median size between the green and red subpopulation sizes is significant, but only

at the 1σ level.

• Each of the three GC subpopulations have significantly different mass functions

(computed from absolute magnitude using a standard mass-to-light ratio) and pho-

tometric ellipticity.

• All three GC subpopulations follow a power law galactocentric radius - GC size trend

with a slope of 0.49. This slope is most likely due to the galaxy potential well that

the GCs reside in. In general, the GCs at small galactocentric radii undergo more

tidal forces and consequently lose more of their of outer stars than GCs at large

galactocentric radii.

These results lead us to conclude that the third (green) GC subpopulation is most likely

physically distinct from both the blue and red GC subpopulations. In light of this con-

clusion we discussed the possible connection of the green GC subpopulation to the Kine-

matically Distinct Core (KDC) (Surma & Bender, 1995; Davies et al., 2001) at the centre

of NGC 4365 and found that connection unlikely given the disparity in physical extent,

5 arcsec for the KDC and 2 arcmin for the green subpopulation. We also discussed the

impact that the existence of three GC subpopulations would have on the current GC for-

mation scenarios. While the major merger (Zepf & Ashman, 1993), multiphase collapse

(Forbes et al., 1997) and accretion (Cote et al., 1998) scenarios primarily aim to explain

the existence of ubiquitous GC bimodality, we find that each of the formation scenarios

6.1. Chapter Summary 121

is able to explain the presence of an intermediate colour (green) subpopulation in a small

percentage of large galaxies.

To further investigate the properties of each GC subpopulation and distinguish between

possible formation scenarios for NGC 4365’s GC system we analysed the velocity of a

subsample of the GCs. The data and analysis are presented in Chapter 4. Spectral

observations were performed with the Keck II/DEep Imaging Multi-Object Spectrograph

(DEIMOS). We found that each of the blue, green and red GC subpopulations rotate in

different directions and discovered a distinct population of low velocity GCs around NGC

4365. In summary:

• The green GC subpopulation rotates in the same direction as the stellar component

of NGC 4365, along the minor photometric axis (i.e. rolling).

• The red GC subpopulation rotation is only significant beyond 2 arcmin for the galaxy

centre and there is rotates perpendicular to the stellar component and in the opposite

direction to the rotation of the KDC.

• The rotation of the blue GC subpopulation twists in direction by ∼ 90◦ from 3 to

7 arcmin. This is consistent with members of the blue subpopulation having been

accreted from several different dwarf galaxies.

• The 13 low velocity GCs around NGC 4365 form a spatially linear feature that is

consistent with the stellar stream presented in Bogdan et al. (2012a).

The green subpopulation does not originate from the galaxy that is causing the stellar

stream feature, as the low velocity GC are not preferentially green in colour and the

rotation of the green subpopulation is perpendicular to the stream. We also discussed the

possibility that NGC 4365’s green GCs were accreted from a single, intermediate sized

galaxy and found this to be unlikely. The number of green GCs suggests a fairly luminous

galaxy (Harris & van den Bergh, 1981; Peng et al., 2008) and the mean colour of the green

GCs suggests a much less luminous galaxy (Peng et al., 2006). We conclude that it is most

likely that the red GCs were formed in situ, the green GCs are also likely to have formed

in situ but earlier in a much more efficient phase of GC formation, and the blue GCs have

been accreted from smaller galaxies over the lifetime of NGC 4342.

Given the evidence of a stellar stream from Bogdan et al. (2012a) we investigated the

GC system of NGC 4342 and the stream between NGC 4365 and NGC 4342 in Chapter 5.

We used publicly available imaging from the MegaCam instrument on the Canada-France-

Hawaii Telescope (CFHT) to identify GCs in the NGC 4365, NGC 4342 and stream region.


We found a statistically significant overdensity of GCs forming a stream from NGC 4365

to NGC 4342 and beyond. We also obtained Keck/DEIMOS spectroscopy of a subsample

of GCs on NGC 4342 and the stream. We added the low velocity GCs around NGC 4365

to the stream sample as the recession velocities are similar and they coincide spatially

with the stellar stream. Analysing the photometric and spectroscopic properties of these

GCs we found:

• The spatial distribution of GCs shows a density peaks at the positions of NGC 4365

and NGC 4342 as well as an elongated feature extending SW of NGC 4365 and

crossing NGC 4342.

• The colour distribution of the GCs around NGC 4342 is indistinguishable from the

colour distribution of GCs spatially coincident with the stellar stream. Their colour

distributions are significantly different from that of NGC 4365.

• Measured recession velocities for combined NGC 4342 and stream GCs match the

recession velocity of NGC 4342.

From these results we conclude that the GCs in the stream are being tidally stripped from

NGC 4342 onto NGC 4365. This result suggests that the stellar stream has been formed

through stripping of stars from NGC 4342 and conflicts with the Bogdan et al. (2012a)

conclusion that NGC 4342 cannot have undergone significant stellar stripping because it

still hosts a large dark matter halo as evidenced by its hot gas halo (Bogdan et al., 2012b).

The question of whether NGC 4342 is being tidally stripped could be resolved with very

deep spectral analysis of the stellar stream but such observations are prohibitively time

consuming.

6.2 Avenues for Further Research

6.2.1 Substructure within galaxy groups and clusters

Recent studies have shown clear evidence of tidal streams around our own Milky Way

Galaxy and around the Andromeda galaxy (M31, McConnachie et al., 2009). These stud-

ies have used very large stellar surveys, across enormous fields-of-view to identify low

surface brightness tidal features using areas of individual star count overdensity. These

are important confirmations of the frequent minor mergers and harassment events pre-

dicted by ΛCDM cosmology. However such observations are limited to the Local Group.

It is possible to find evidence of very low surface brightness tidal features, on the scale

6.2. Avenues for Further Research 123

of galaxy interaction, much further. Mihos et al. (2005) and Bogdan et al. (2012a) have

presented images of tidal streams, using low surface brightness integrated stellar light as

far as the Virgo Cluster and Tal et al. (2009) found tidal features in galaxies more than

30 Mpc away. However, this work requires a prohibitive amount of observing time (100+

nights on the UK Schmidt telescope on Kitt Peak, and summation of the entire Sloan Dig-

ital Sky Survey, respectively) and extremely careful monitoring of observing conditions to

avoid introduction of spatially coherent noise into the imaging.

In Chapter 5 of this thesis we uncovered a GC overdensity (Blom et al., 2013) overlap-

ping the low surface brightness stellar feature presented in Bogdan et al. (2012a). Figure

6.1 shows the smoothed spatial distribution of GCs compared with the low surface bright-

ness stream feature. Lee et al. (2010) presented a detection of large scale structure in the

GC distribution of the Virgo Cluster but did not have the spatial resolution to identify

particular tidal overdensities. The GC data was obtained from publicly available, rela-

tively shallow imaging obtained with the MegaCam imager on the Canada-France-Hawaii

Telescope (CFHT). Given the relative binding energies for GCs and stars to the galaxy

potential we expect GCs to be stripped at least as easily as stars (and possibly more eas-

ily than stars) during a merger or accretion event, and therefore expect GC overdensities

to follow low surface brightness stellar indicators of ongoing accretion or merging. This

expectation seems to be supported by this discovery in the W ′ group and the fact that

similar GC overdensities have been detected by Mackey et al. (2010) on the stellar streams

observed around M31. The benefit of observing GC overdensities is that the bulk of GCs,

at the Virgo Cluster distance, are brighter than i = 24 mag arcsec−2 (i = 24 mag over a

seeing disc 1 arcsec2 in area), more than 4 magnitudes brighter in surface brightness than

the stellar stream features. Even with shallower imaging, significant numbers of GCs can

be identified and overdensities, if present, detected.

Many theories of galaxy formation suggest that galaxy groups accelerate the evolution

of galaxies, due to the increased frequency of galaxy interactions. This search for intra-

group structure is a direct method to evaluate that claim. It is especially important to

uncover the interaction processes for early type galaxies, thought to undergo multiple wet

and dry mergers during their lifetime.

6.2.2 Searching for the causes of significant kinematic misalignment

Most elliptical galaxies rotate with a kinematic position angle very similar to their photo-

metric position angle. As shown in Figure 6.2, the ATLAS3D team (Krajnovic et al., 2011)

found very few galaxies with extreme (∼ 90◦) kinematic misalignment, and only 2 of those


Figure 6.1: Reproduction of Figures 5.13 and 5.3 for direct comparison. (Top) Avery deep image which reveals a ∼ 300 kpc long stream of stars (Bogdan et al., 2012a).(Bottom) Smoothed GC surface density from CFHT data within the dashed region ofthe deep image. The GCs reveal a similar spatial distribution to the stellar stream, withconcentrations around NGC 4365 and NGC 4342. Refer to Chapter 5 for more details.

6.2. Avenues for Further Research 125

Figure 6.2: Reproduction of Figure 8a from Krajnovic et al. (2011). It shows the kine-matic misalignment angle (difference between galaxy photometric and kinematic axes) asa function of the galaxy ellipticity. NGC 4365 (hosting 3 globular cluster subpopulations)and NGC 4406 (M86) are circled, both host a Kinematically Distinct Core and are thedominant galaxy in their respective groups.


(NGC 4365 and NGC 4406) also have a Kinematically Distinct Core feature. Both NGC

4365 and NGC 4406 are the most massive galaxy in their groups, just beyond the Virgo

Cluster. The GC system in NGC 4406 has not yet been studied kinematically, neither has

a detailed and careful study of its photometric properties been done with deep, wide-field

imaging. Blom et al. (2012a,b) found that NGC 4365 hosts a very unusual colour trimodal

GC system with distinct kinematic features associated with each GC subpopulation. The

question, Does stellar kinematic misalignment correlate with n > 2 GC subpopulations?,

is one first step to finding the cause of both of these unusual galaxy properties. Photo-

metric analysis of the GC subpopulations in the systems shown to have extreme kinematic

misalignment would provide an answer and insight into the assembly history of these mis-

aligned galaxies. The answer to another question, Are unusual GC systems or extreme

kinematic misalignment present preferentially in galaxy groups?, could provide significant

insight into the environments that influence the formation of unusual galaxies. The same

group galaxies targeted for wide-field studies, in search of GC overdensities indicating

tidal interactions, would provide some answers to this question. The sample of group GC

systems could also be compared to the GC systems analysed as part of the SAGES Legacy

Unifying Globulars and Galaxies Survey (SLUGGS)1 .

6.3 Broad impact of this work

There are many unanswered questions regarding the formation and assembly history of the

giant elliptical galaxy NGC 4365. However, this thesis has definitively shown that NGC

4365 has three distinct GC subpopulations and presented a possible formation scenario.

We propose that green GCs formed within the central halo that would become NGC 4365,

but unusually early during the GC formation era and that the red GCs also formed in situ

as NGC 4365 was forming but slightly later. We determine it most likely that NGC 4365

accreted blue GCs from smaller galaxy halos over its lifetime. This interpretation is in

best agreement with the accretion scenario of GC formation (Cote et al., 1998), borrowing

ideas from the multiphase collapse scenario (Forbes et al., 1997) and agrees well with the

results of recent cosmological simulations of galaxy and GC system formation (Tonini,

2013).

The spatial and mass resolution as well as simulation size required to simulate the

formation of GCs and GC systems at the same time as the formation and evolution of their

parent galaxies, has evaded galaxy modellers thus far. With continuing improvements in

computing facilities it is likely that these technical constraints will soon no longer hamper1http://sluggs.swin.edu.au

6.3. Broad impact of this work 127

the progress of GC galaxy evolution studies. It is important for the observations of GC

systems to also continue to probe deeper into the properties of GC systems. GC systems

contain a wealth of information about the chemical environment in the early universe when

they were formed, as well as dynamical tracers of the interactions their host galaxies have

undergone. With more wide-field data the properties of large galaxies and galaxy groups

in the nearby universe can be uncovered in greater detail. With deeper data we can probe

GC systems further away to improve statistical analysis of GC system properties as well

as investigations of nearby GC systems with more accurate photometry and spectroscopy.

For example, probing most of the way down the GC luminosity function might allow the

intrinsic skewness and kurtosis of GC subpopulations to be determined.

This thesis is an early example of the detailed analysis of galaxy evolutionary history

that can be done with analysis of its GC system. Future studies can build on the tech-

niques presented here to study a wider range of galaxies and come closer to answering our

questions about how galaxies formed and became the beautiful object we see in the sky

today.

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AStatistical tests for trimodality

A.1 Kaye’s Mixture Model algorithm

The most common tool to determine the statistical significance of GC bimodality is the

Kaye’s Mixture Model (KMM) code (Ashman et al., 1994), designed to calculate the

‘p’ statistic for the significance of a homoscedastic bimodal Gaussian distribution being

preferred over a unimodal Gaussian distribution. KMM is also capable of determining the

significance of heteroscedastic multimodal distributions being preferred over a unimodal

Gaussian distribution but the ‘p’ statistic in these cases is an approximation. Applied to

our GC colour distribution for NGC 4365 both bimodal and trimodal distributions result in

a ‘p’ statistic less than 10−4 (a probability larger than 99.99 per cent that the multimodal

distributions are preferred over the unimodal distribution). The traditional KMM code is

however not capable of determining the probability that a trimodal distribution is preferred

over a bimodal distribution.

Using an extension to the traditional KMM code, written by Dr Matthew Owers (Ow-

ers et al., 2011), we were able to assess a probability for three Gaussian modes over two

Gaussian modes bearing in mind that the code was untested for application to GC colour

distributions. We attempted to test the code at the same time as using it for our analysis

by including checks on how the code performed with varying GC input numbers, analy-

sis of other galaxies’ GC distributions and redundant calculations for the probability of

four modes over three modes and bimodality over unimodality. See results in Table A.1.

Comparing results for NGC 4365 with NGC 1407 (data from Spitler et al. 2011 in prep.

also used in Romanowsky et al. 2009; Foster et al. 2010; Forbes et al. 2011) the extended

KMM test finds that both are trimodal (NGC 4365 with a probability of 99.7 per cent

and NGC 1407 with a probability larger than 99.9 per cent) but there is little evidence

in the literature for NGC 1407 being trimodal. When the number of GCs is halved for

139

140 Appendix A. Statistical tests for trimodality

NGC 4365 (data from Peng et al., 2006) the probability of trimodality decreases below 95

per cent (to 91.8 per cent), even though the smaller sample is more central and we expect

trimodality to be more prominent there. It is probable that in the case of NGC 1407 the

preference for trimodality is due to significant skewness in the subpopulation distributions

(caused by strong radial gradients in subpopulation mean colour, see Forbes et al. 2011).

A.1. Kaye’s Mixture Model algorithm 141

Gal

axy

Un

imod

alp

Bim

od

alp

Tri

mod

alp

Qu

adru

mod

alµ

σn

(2ov

er1)

µσ

n(3

over

2)µ

σn

(4ov

er3)

µσ

n

Com

par

ing

gala

xie

sw

ith

Su

bar

u/S

-Cam

ph

otom

etry

andg′ −

i′co

lou

rsw

her

ei′

623

NG

C43

65

0.95

0.16

1679

0.00

0

0.79

0.05

642

0.00

3

0.78

0.04

600

0.25

2

0.78

0.05

642

1.05

0.12

1037

0.96

0.09

567

0.96

0.09

298

1.13

0.09

512

1.01

0.1

302

1.15

0.08

437

NG

C14

07

0.92

0.16

1604

0.00

0

0.79

0.06

784

0.00

0

0.76

0.04

616

-

0.77

0.04

696

1.06

0.09

820

0.93

0.11

562

0.91

0.12

761.

130.

0642

60.

980.

141

51.

140.

0641

7C

omp

arin

gga

laxie

sw

ith

HS

T/A

CS

ph

otom

etry

andg−z

colo

urs

NG

C43

651.

120.

2216

58

0.00

0

0.89

0.08

605

0.00

3

0.88

0.08

550

0.59

6

0.88

0.08

584

1.25

0.15

1053

1.12

0.12

488

1.11

0.12

203

z6

241.

130.

1262

01.

190.

1434

8al

lpo

int.

1.36

0.11

523

NG

C43

651.

190.

2490

5

0.00

0

0.94

0.13

318

0.08

2

0.92

0.07

120

0.91

5

0.91

0.15

451.

330.

1758

70.

980.

1719

20.

920.

0720

4z

624

1.33

0.17

593

1.08

0.19

61ce

nt.

poin

t.1.

340.

1759

5

M60

1.24

0.26

795

0.00

0

0.98

0.13

320

0.32

8

0.85

0.11

51

-

0.83

0.10

431.

430.

1547

51.

000.

0925

51.

000.

0927

2z

624

1.41

0.16

489

1.29

0.08

147

cent

.po

int.

1.49

0.13

333

Tab

leA

.1:

Res

ults

from

the

exte

nded

KM

Mte

stfo

rte

stin

gth

esi

gnifi

canc

ew

ith

whi

chm

+1

mod

esar

epr

efer

red

overm

mod

esin

colo

ur.

For

each

mod

eth

em

ean

valu

e(µ

),w

idth

(σ)

and

num

ber

ofob

ject

sas

sign

ed(n

)ar

esh

own

and

betw

een

each

mod

alit

yth

e‘p

’st

atis

tic

issh

own.


VCC number 1226 1316 731 1535Other name M49 M87 NGC 4365 NGC 4526

M49 0.79 0.86 1.6× 10−7 1.4× 10−3

M87 - 0.45 4.6× 10−9 6.0× 10−4

NGC 4365 - - - 9.2× 10−3

Table A.2: KS test results comparing galaxies in the ACS Virgo Cluster Survey. The‘p’ statistic shown is an inverse measure of the probability that GCs in the differentgalaxies are drawn from different underlying colour distributions. Only M49 and M87are inconsistent with being drawn from different colour distributions and hence could besaid to have the same underlying colour distribution. Other galaxy comparisons showa probability greater than 99 per cent of being drawn from different underlying colourdistributions.

A.2 The Kolmogorov-Smirnov test

The Kolmogorov-Smirnov (KS) test shows clearly that most giant elliptical galaxies have

GC colour distributions that are significantly different from each other (subpopulation

peak colours and widths vary between galaxies, see Table A.2) (using GC catalogues

from Peng et al., 2006). A sample of galaxies with reasonably large GC numbers were

chosen to compare to each other and most galaxies show a probability of larger than 99

per cent of being drawn from a colour distribution different to any other galaxy in the

sample. Two very large galaxies (M87 and M49) are consistent with being drawn from the

same underlying colour distribution. It is clear that each galaxy’s GC system is formed

independently of any other GC system but that similar galaxy properties can result in

similar GC systems.

We also used this test to compare colour distributions simulated from bimodal and tri-

modal descriptions of the data (as output by KMM) with the actual GC colour distribution

and found that the observed GC distribution is significantly different from both simulated

distributions. Table A.3 shows that for all comparisons the KS test shows probabilities of

Simulated Actual colour distributionsdistributions ACS S-CamBimodal 10−95 10−256

Trimodal 10−99 10−262

Table A.3: KS test results comparing simulated bimodal and trimodal distributionswith NGC 4365’s colour distribution. The ‘p’ statistic shown is an inverse measure of theprobability that the simulated distributions are drawn from different colour distributionsto the actual ones.

A.3. Chi-Squared minimization 143

almost 100 per cent that the simulated and actual distributions are significantly different.

Neither of the simulated distributions is a good enough description of the observed distri-

bution, likely because they cannot take into account the skewness and peakiness (kurtosis)

of the observed distributions. The colour modes in GC systems are not perfectly Gaussian

and therefore this method of analysis cannot distinguish between bimodal and trimodal

Gaussian descriptions of the colour distribution to determine which is more likely.

A.3 Chi-Squared minimization

Comparing the bimodal and trimodal results for a reduced Chi-Squared (χ2) minimization

is dependent on binning the data in colour. We have chosen to do this binning via an

Epanechnikov kernel smoothing of the data rather than an histogram. With different

colour bin sizes the statistical results will also be different. We have chosen bin sizes for

the Epanechnikov kernel that are equal to the colour errors in the data. Kernel smoothed

data is overpotted with bimodal and trimodal distributions in Figures A.1 and A.2.

Using the entire radial range of our data we can achieve the best number statistics but

the results are not conclusive. For the ACS GCs, where we have better colour resolution,

the best-fitting trimodal Gaussian distribution has a slightly smaller reduced χ2 value

than the best-fitting bimodal distribution but the best fit third mode is very close to

blue modes and contains many fewer objects. This ‘third mode’ is not best described

as a third GC subpopulation but as an indication that the two modes of the underlying

GC distribution are not purely Gaussian but probably Gaussians with both skewness and

kurtosis. We do see a significantly smaller reduced χ2 value and a central third mode with

a large proportion of the GCs in the S-Cam catalogue.


Figure A.1: Results of bimodal and trimodal fits to the Epanechnikov kernel smoothingof the HST/ACS GC colour distribution. The distribution is plotted in grey, showingpoissonian errors, and the individual Gaussians as well as the summation of all three areoverplotted for both the bimodal (dashed lines) and trimodal (dotted lines) fits. Thereduced χ2 value is 1.40 for the bimodal case and 1.26 for the trimodal case.

Figure A.2: Results of bimodal and trimodal fits to the Epanechnikov kernel smoothingof the Subaru/S-Cam GC colour distribution. The distribution is plotted in grey, showingpoissonian errors, and the individual Gaussians as well as the summation of all three areoverplotted for both the bimodal (dashed lines) and trimodal (dotted lines) fits. Thereduced χ2 value is 2.57 for the bimodal case and 1.07 for the trimodal case.

BSurface density data

B.1 Surface density data

The GC surface density data are recorded in Table B.1.

145

146 Appendix B. Surface density data

rgal Total err rgal Blue errB Red errR rgal Green errG(arcmin−2) (arcmin−2) (arcmin−2) (arcmin−2)

GC candidate radial surface density obtained from HST/ACS0.20 119 30 0.62 12.5 2.3 38.0 4.1 0.63 24.6 3.30.32 125 20 1.2 10.2 1.5 20.0 2.1 1.2 9.9 1.50.50 81 11 1.8 8.5 1.1 14.5 1.5 2.1 3.51 0.460.75 66.5 6.8 2.4 5.48 0.74 7.57 0.86 3.4 1.92 0.271.10 43.4 4.0 3.0 5.27 0.69 5.06 0.671.50 31.6 2.9 3.8 4.03 0.52 6.22 0.641.90 26.6 2.4 5.0 3.47 0.40 2.44 0.332.30 14.9 1.62.75 15.9 1.43.15 13.6 1.5

GC candidate radial surface density obtained from Subaru/S-Cam1.10 43.4 4.0 1.2 11.4 1.6 21.8 2.2 1.5 5.86 0.731.50 32.0 2.9 1.8 9.0 1.2 12.3 1.4 2.7 2.25 0.321.90 24.9 2.3 2.4 6.39 0.79 8.21 0.90 4.2 1.05 0.162.30 16.4 1.7 3.0 5.23 0.69 5.85 0.73 5.8 0.74 0.112.75 14.9 1.3 3.8 4.06 0.45 4.79 0.49 8.0 0.399 0.0563.25 11.8 1.1 4.5 3.88 0.40 2.94 0.35 10.5 0.318 0.0443.75 10.70 0.95 5.4 3.34 0.36 1.93 0.28 13 0.328 0.0384.25 8.54 0.80 6.2 2.52 0.25 1.85 0.22 16 0.174 0.0244.75 7.57 0.71 7.4 1.77 0.17 1.18 0.145.25 5.88 0.60 8.6 1.48 0.15 0.90 0.125.75 5.70 0.56 9.9 1.32 0.13 0.760 0.0996.30 5.05 0.46 11.1 1.08 0.11 0.594 0.0826.90 3.77 0.38 12.4 0.645 0.081 0.717 0.0867.50 3.36 0.34 13.8 0.653 0.071 0.530 0.0648.20 2.47 0.24 15.2 0.582 0.067 0.431 0.0588.90 3.10 0.30 17.0 0.438 0.051 0.434 0.0519.60 2.71 0.24 20.5 0.362 0.046 0.502 0.05410.5 1.83 0.1711.5 2.10 0.1712.5 1.74 0.1513.5 1.67 0.1415.0 1.49 0.1017.2 1.274 0.08720.2 1.033 0.093

Table B.1: GC radial surface density data for the total GC system and each subpopula-tion. We show surface density with different radial binning (columns typeset in bold) forthe total GC system, the blue and red subpopulations and the green subpopulation.

CAdditional supopulation kinematic fits

C.1 Total subpopulation kinematic fits

The colour peaks and widths of the blue, green and red subpopulations cause significant

overlap in the colour ranges of the subpopulations. We explore the effect of splitting

the populations in different ways to determine the best way to minimize the contamina-

tion between samples of the subpopulations and maximize the number of GCs in each

subpopulation sample.

The first subpopulation split definition is based on the probability that each GC is

assigned of being in either the blue, green or red subpopulations by Blom et al. (2012a).

They assigned each GC a probability of belonging to the blue, green and red subpopu-

lations (PBlue + PGreen + Pred = 1) based on its colour and galactocentric radius. We

define medium probability samples with P > 0.80 to represent the three subpopulations

and find 53 blue, 53 green and 65 red GCs with line-of-sight radial velocity measurements.

These samples are mostly free of contamination from the other subpopulations but have

a relatively small number of GCs to be analysed for each subpopulation.

The next subpopulation split definitions cut the GCs into three subsamples at different

g′ − i′ colours. For the first colour cut split definition we split the sample at g′ − i′ = 0.9

and g′−i′ = 1.1 where we see dips in number of the photometric sample colour distribution

(see Fig. 3.9). This subpopulation split definition divides the kinematic sample roughly

equally between the three subpopulations. The next two colour cut split definitions widen

the green sample definition first to redder colours (colour cut 2) and secondly to bluer

colours (colour cut 3). The repeated fits for the blue (in colour cut 1 and colour cut 2)

and red samples (in colour cut 1 and colour cut 3) show the level to which results are

affected by the randomisation in the bootstrap technique of determining uncertainties.

We also show the kinematic fits for both cases of the kinematic axis ratio. In the first

147

148 Appendix C. Additional supopulation kinematic fits

case qkin is fixed to the photometric value for each GC subpopulation (qkin = qphotom from

Blom et al., 2012a) and in the second the axis ratio is fixed to unity (qkin = 1). We do not

find any significant differences in the fitted kinematics between the two axis ratio cases

and conclude that this kinematic fitting method is not very sensitive to the kinematic axis

ratio. In all split definitions for all subpopulations, the kinematic fits for the two cases are

consistent within 1σ uncertainty and the computed Λ/ndf values are equal. All further

kinematic fits are done in the case where qkin = 1 as this is the more general case.

C.1. Total subpopulation kinematic fits 149

Sp

lit

Def

nV

rot

σP

Akin

Vrot

σP

Akin

Vrot

σP

Akin

Defi

nit

ion

qkin

(km

s−1)

(km

s−1)

(◦)

(km

s−1)

(km

s−1)

(◦)

(km

s−1)

(km

s−1)

(◦)

PBlue>

0.8

(53)

PGreen>

0.8

(53)

PRed>

0.8

(65)

q phot

58±

52

23

178±

10

17

243±

24

18

103±

57

41

228±

12

20

141±

21

15

39±

56

13

218±

14

23

175±

66

51

med

.p

rob

.1

49±

48

18

179±

10

17

254±

38

54

83±

57

29

229±

13

22

145±

33

31

39±

52

11

218±

14

22

174±

62

53

g′ −

i′<

0.9

(80)

0.9<g′ −

i′<

1.05

(77)

g′ −

i′>

1.05

(79)

q phot

56±

41

22

188±

10

15

251±

12

11

96±

43

33

217±

9 17

142±

25

16

43±

45

16

216±

12

18

167±

51

44

col.

cut

11

40±

41

18

189±

9 15

267±

40

50

81±

39

24

217±

10

15

144±

27

23

43±

45

15

216±

13

19

167±

51

40

g′ −

i′<

0.9

(80)

0.9<g′ −

i′<

1.1

(100

)g′ −

i′>

1.1

(56)

q phot

56±

43

21

188±

9 14

251±

17

12

81±

43

29

221±

9 13

126±

17

14

95±

50

32

202±

15

25

195±

23

21

col.

cut

21

40±

46

20

189±

8 15

267±

43

55

67±

40

26

222±

9 14

124±

28

24

94±

48

34

202±

15

26

194±

23

24

g′ −

i′<

0.85

(61)

0.85<g′ −

i′<

1.05

(96)

g′ −

i′>

1.05

(79)

q phot

70±

47

25

178±

9 16

248±

27

13

83±

41

30

216±

10

14

134±

16

14

43±

44

19

216±

12

19

167±

49

48

col.

cut

31

67±

44

27

178±

10

16

269±

28

34

67±

36

23

217±

10

15

134±

31

25

43±

45

15

216±

12

20

167±

57

44

Tab

leC

.1:

Res

ults

for

kine

mat

icfit

sto

vari

ous

divi

sion

sof

the

thre

eG

Csu

bpop

ulat

ions

.W

eta

bula

teth

ero

tati

onve

loci

ty(Vrot)

,ve

loci

tydi

sper

sion

(σ)

and

kine

mat

icpo

siti

onan

gle

(PAkin

)fo

rea

chsu

bpop

ulat

ion

inal

lth

esp

litde

finit

ions

.R

esul

tsar

esh

own

whe

reth

eax

isra

tio

(qkin

=q phot)

isfix

edto

the

phot

omet

ric

valu

esfo

rea

chsu

bpop

ulat

ion

(qblue

=0.

56,q

green

=0.

70,q

red

=0.

97an

dq gal

=0.

75)

and

whe

reit

isfix

edto

1.N

umbe

rsof

GC

sin

each

subp

opul

atio

nre

pres

enta

tive

sam

ple

are

give

nin

brac

kets

afte

rth

ede

finit

ion

para

met

ers.

The

maj

orax

isof

the

NG

C43

65st

ella

rlig

htis

42◦

or22

2◦.

Pro

babi

lity

isde

note

dw

ithPColour.

Val

ues

for

the

best

colo

urcu

tsar

ehi

ghlig

hted

inbo

ld.

150 Appendix C. Additional supopulation kinematic fits

SplitBlue Green Red

Definitionmedium probability 12.1 12.6 12.3colour cut 1 11.9 11.8 12.2colour cut 2 11.9 15.3 12.2colour cut 3 12.0 19.5 12.2

Table C.2: Minimisation parameter divided by degrees of freedom (Λ/ndf) for kinematicfits to various divisions of the three GC subpopulations. Values for the best colour cutsare highlighted in bold.

The best kinematic fits are found for the blue GCs in colour cut 3, green GCs in

colour cut 1 and red GCs in colour cut 2. We determine this best kinematic fit to be

the subpopulation sample has the most constrained position angle (for q=1), see Table

C.1, and lowest value of Λ/ndf , see Table C.2. The blue subpopulation representative

sample in colour cut 3 Λ/ndf is marginally larger than in colour cut 1 or 2 but the

position angle range is significantly smaller (62◦) than in either colour cut 1 or 2 (90◦ and

98◦ respectively). The green subpopulation representative sample position angle range in

colour cut 1 (50◦) is only slightly smaller than that of colour cut 2 (52◦) but the Λ/ndf is

significantly smaller. The red subpopulation representative sample Λ/ndf is the same for

each colour cut but the position angle range is significantly smaller in colour cut 2 (47◦)

than either of the other colour cuts (91◦ and 101◦) where the PAkin is not constrained to

< 90◦.

C.2 Radial kinematic fits with fixed position angle

Here we compare the radial kinematic fits for the three subpopulations in the case where

the PA is fixed to the value fitted for the whole subpopulation (Fig. C.1) to the case where

the PA is allowed to vary with radius (Fig. 4.9). The fitted values for σ do not change at

all when the position angle of rotation is fixed. The uncertainties in Vrot, are larger when

PAkin is fixed than when the PAkin is allowed to vary. If the reduction in number of free

parameters, in the case where PAkin is fixed, does allow a more stable fit to the Vrot and

σ we would expect the uncertainties (68 per cent confidence intervals) to be smaller there.

Since the opposite is true it is more likely that the fit is better where the PAkin is allowed

to vary, and since the PAkin is well constrained at almost all radii in that case it is also

likely that the Vrot is also more reliable than the case where PAkin is fixed.

The one exception is for red GCs less than 2 arcmin from the galactic centre. The

varying PAkin is not well constrained there and rotation parameter (Vrot/σ) fails the

C.2. Radial kinematic fits with fixed position angle 151

Figure C.1: Kinematics as a function of radius for NGC 4365’s three GC subpopulations.GC subpopulations are defined using the best colour cuts highlighted in Table C.1 andthe PAkin is fixed to the values found there. Symbols are as in Fig. 4.9.

criterion in Equation 4.5 (Strader et al., 2011). In the case where PAkin is fixed Vrot is

correctly fitted to be consistent with zero. The decrease in Vrot at certain other points

in Fig. C.1 (e.g. ∼ 3 arcmin for the blue subpopulation) is due to the offset between the

value the PAkin was set at and the PAkin at which the system is actually rotating at that

radius.

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