Jets and jet substructure 4:

substructure

1

Gavin Salam (CERN)with extensive use of material by Matteo Cacciari

and Gregory Soyez

TASIJune 2013

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

Two things that make jets@LHC special

The large hierarchy of scales√s ⨠ MEW

The huge pileupnpileup ~ 20 – 40

[These involve two opposite extremes: low pt and high pt,which nevertheless talk to each other]

2

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013 3

E.g. X ! tt̄ resonances of varying di!culty[1 jet ! 2 partons]

RS KK resonances ! tt̄, from Frederix & Maltoni, 0712.2355

NB: QCD dijet spectrum is " 103 times tt̄Jets lecture 3 (Gavin Salam) CERN Academic Training March/April 2011 10 / 29

e.g. ttbar resonances

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

Boosted massive particles ! fat jets

Normal analyses: two quarks fromX ! qq̄ reconstructed as two jets

jet 1

jet 2

X at restX

High-pt regime: EW object Xis boosted, decay is collimated,

qq̄ both in same jet

singlefat jet

z

(1−z)

boosted X

Happens for pt ! 2m/R

pt ! 320 GeV for m = mW , R = 0.5

Gavin Salam (CERN/LPTHE/Princeton) Jets in Higgs Searches HC2012 2012-11-18 19 / 29

4

Boosted EW scale objects

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

Very active research field

5

Some taggers and jet-substructure observables

Jet Declustering

Jet Shapes

Matrix−Element

Seymour93

YSplitter

Mass−Drop+Filter

JHTopTagger TW

CMSTopTagger

N−subjettiness (TvT)

CoM N−subjettiness (Kim)

N−jettiness

HEPTopTagger(+ dipolarity)

Trimming

Pruning

Planar Flow

Twist

ATLASTopTagger

Templates

Shower Deconstruction

Qjets

Multi−variate tagger

ACF

apologies for omitted taggers, arguable links, etc.

Gavin Salam (CERN/Princeton/CNRS) Boost Theory Summary Boost 2012-07-27 6 / 33

Some of the tools developedfor boosted W/Z/H/top

reconstruction

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

Papers on jet substructure

6

More than 100 papers since 2008

(+ some background noise)

Number of papers containing the words ‘jet substructure’

Mike Se

ymou

r

Butte

rwort

h, Cox

, Fors

haw

Pioneered by M. Seymour in the early ‘90s

0

1

2

3

4

5

6

7

1990 1995 2000 2005 2010 2015

pape

rs /

mon

th

year

Papers containing "jet substructure"+ pioneering papers by Mike Seymour in 1991 and 1994(Source: INSPIRE)

Exploded around 2008

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

Extensive experimental work

7

From a list compiledfor a recent workshopat Perimeter Institute

Many more analyses in the pipeline

ATLAS Public Results• Large-R, groomed jets with pile-up• Large-R jets with substructure• Quark/gluon jets (see also this link)• Jet substructure at LHC7• Jet properties for boosted searches

Resonance searches• Boosted top (hadronic)• Boosted top (semileptonic)• Three-jet resonance (gluino RPV)• Two-jet resonance (sgluon)

CMS Public Results• Jet substructure in CMS• Subjet multiplicity• Jet mass and grooming

Resonance searches:• Boosted top (hadronic)• Boosted top (semileptonic)• Boosted W/Z

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

Jet masses

8

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0 50 100 150 200

1/N

dN

/dm

jet [

GeV

-1]

mjet [GeV]

Wj events

pt,jets > 700 GeV

anti-kt, R = 0.7

pp

7 T

eV

, Pyth

ia 6

.4, n

oU

E

Pythia, underlying event switched off

Look at jet mass distribution for 2 leading jets in

‣ pp → W+jet events

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

Problem #1a: QCD jets have masses too

9

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

0 50 100 150 200

1/N

dN

/dm

jet [

GeV

-1]

mjet [GeV]

qq ! qq events

pt,jets > 700 GeV

anti-kt, R = 0.7

pp

7 T

eV

, Pyth

ia 6

.4, n

oU

E

Look at jet mass distribution for 2 leading jets in

‣ pp → W+jet events‣ qq → qq events

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

Problem #1b: there are lots of QCD jets

10

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

0 50 100 150 200

1/N

dN

/dm

jet [

GeV

-1]

mjet [GeV]

qq ! qq + Wj mixture

pt,jets > 700 GeV

anti-kt, R = 0.7

pp

7 T

eV

, Pyth

ia 6

.4, n

oU

E

Look at jet mass distribution for 2 leading jets in

‣ pp → W+jet events‣ qq → qq events‣ mixture of the two,

in rough proportion

Jet mass gives clear sign of massive particles inside the jet; but QCD jets are massive too —

must learn to reject them

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

Problem #2: jet mass v. sensitive to PU

11

m/N

dN

/dm

jet

mjet [GeV]

Wj eventspt,jets > 700 GeV

anti-kt, R = 0.7

pp

7 T

eV

, Pyth

ia 6

.4, n

oU

E

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0 50 100 150 200

+ !10" pileup

Jet mass is extremely sensitive to pileup

→ loss of mass resolution

→ loss of ability to find signals

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

Tagging & Grooming

12

Tagging• reduces the background, leaves much of signal

Grooming• improves signal mass resolution (removing pileup,

etc.), without significantly changing background & signal event numbers

Two widely used termsthough there’s not a

consensus aboutwhat they mean

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013 13

One core idea for tagging

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

Inside the jet mass

14

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10 100

1/N

dN

/dlo

g(m

jet)

mjet [GeV]

QCD Jet Mass distributionPythia 6.4, qq!qq, no UE

anti-kt, R=0.7

LHC, 7 TeV

pt,jets > 700 GeV

Inside the jet mass[1 jet ! 2 partons]

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10 100

1/N

dN

/dlo

g(m

jet)

mjet [GeV]

QCD Jet Mass distributionPythia 6.4, qq→qq, no UE

anti-kt, R=0.7LHC, 7 TeV

pt,jets > 700 GeV

QCD jet mass distribution has theapproximate

dN

d lnm! !s ln

ptR

m" Sudakov

Work from ’80s and ’90s

+ Almeida et al ’08

The logarithm comes from integralover soft divergence of QCD:

! 12

m2

p2t R2

dz

z

A hard cut on z reduces QCD back-ground & simplifies its shape

Jets lecture 3 (Gavin Salam) CERN Academic Training March/April 2011 15 / 29

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

Inside the jet mass

14

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10 100

1/N

dN

/dlo

g(m

jet)

mjet [GeV]

QCD Jet Mass distributionPythia 6.4, qq!qq, no UE

anti-kt, R=0.7

LHC, 7 TeV

pt,jets > 700 GeV

10 100

mjet [GeV]

0.01

0.1

1

z

2-bodyphasespace

Inside the jet mass[1 jet ! 2 partons]

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10 100

1/N

dN

/dlo

g(m

jet)

mjet [GeV]

QCD Jet Mass distributionPythia 6.4, qq→qq, no UE

anti-kt, R=0.7LHC, 7 TeV

pt,jets > 700 GeV

QCD jet mass distribution has theapproximate

dN

d lnm! !s ln

ptR

m" Sudakov

Work from ’80s and ’90s

+ Almeida et al ’08

The logarithm comes from integralover soft divergence of QCD:

! 12

m2

p2t R2

dz

z

A hard cut on z reduces QCD back-ground & simplifies its shape

Jets lecture 3 (Gavin Salam) CERN Academic Training March/April 2011 15 / 29

Inside the jet mass[1 jet ! 2 partons]

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10 100

1/N

dN

/dlo

g(m

jet)

mjet [GeV]

QCD Jet Mass distributionPythia 6.4, qq→qq, no UE

anti-kt, R=0.7LHC, 7 TeV

pt,jets > 700 GeV

10 100mjet [GeV]

0.01

0.1

1

z

2-bodyphasespace

QCD jet mass distribution has theapproximate

dN

d lnm! !s ln

ptR

m" Sudakov

Work from ’80s and ’90s

+ Almeida et al ’08

The logarithm comes from integralover soft divergence of QCD:

! 12

m2

p2t R2

dz

z

A hard cut on z reduces QCD back-ground & simplifies its shape

Jets lecture 3 (Gavin Salam) CERN Academic Training March/April 2011 15 / 29

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

Inside the jet mass

15

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10 100

1/N

dN

/dlo

g(m

jet)

mjet [GeV]

QCD Jet Mass distributionPythia 6.4, qq!qq, no UE

anti-kt, R=0.7

LHC, 7 TeV

pt,jets > 700 GeV

after cut on z > 0.25

(a la BERS)

10 100

mjet [GeV]

0.01

0.1

1

z

2-bodyphasespace

cut on z

keep

reject

Inside the jet mass[1 jet ! 2 partons]

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10 100

1/N

dN

/dlo

g(m

jet)

mjet [GeV]

QCD Jet Mass distributionPythia 6.4, qq→qq, no UE

anti-kt, R=0.7LHC, 7 TeV

pt,jets > 700 GeV

QCD jet mass distribution has theapproximate

dN

d lnm! !s ln

ptR

m" Sudakov

Work from ’80s and ’90s

+ Almeida et al ’08

The logarithm comes from integralover soft divergence of QCD:

! 12

m2

p2t R2

dz

z

A hard cut on z reduces QCD back-ground & simplifies its shape

Jets lecture 3 (Gavin Salam) CERN Academic Training March/April 2011 15 / 29

Inside the jet mass[1 jet ! 2 partons]

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10 100

1/N

dN

/dlo

g(m

jet)

mjet [GeV]

QCD Jet Mass distributionPythia 6.4, qq→qq, no UE

anti-kt, R=0.7LHC, 7 TeV

pt,jets > 700 GeV

10 100mjet [GeV]

0.01

0.1

1

z

2-bodyphasespace

QCD jet mass distribution has theapproximate

dN

d lnm! !s ln

ptR

m" Sudakov

Work from ’80s and ’90s

+ Almeida et al ’08

The logarithm comes from integralover soft divergence of QCD:

! 12

m2

p2t R2

dz

z

A hard cut on z reduces QCD back-ground & simplifies its shape

Jets lecture 3 (Gavin Salam) CERN Academic Training March/April 2011 15 / 29

Inside the jet mass[1 jet ! 2 partons]

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10 100

1/N

dN

/dlo

g(m

jet)

mjet [GeV]

QCD Jet Mass distributionPythia 6.4, qq→qq, no UE

anti-kt, R=0.7LHC, 7 TeV

pt,jets > 700 GeV

after cut on z > 0.25

(a la BERS)

10 100mjet [GeV]

0.01

0.1

1

z

2-bodyphasespace

cut on z

keep

reject

QCD jet mass distribution has theapproximate

dN

d lnm! !s ln

ptR

m" Sudakov

Work from ’80s and ’90s

+ Almeida et al ’08

The logarithm comes from integralover soft divergence of QCD:

! 12

m2

p2t R2

dz

z

A hard cut on z reduces QCD back-ground & simplifies its shape

Jets lecture 3 (Gavin Salam) CERN Academic Training March/April 2011 15 / 29

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

Inside the jet mass

16

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10 100

1/N

dN

/dlo

g(m

jet)

mjet [GeV]

QCD Jet Mass distributionPythia 6.4, qq!qq, no UE

anti-kt, R=0.7

LHC, 7 TeV

pt,jets > 700 GeV

after cut on z > 0.25

(a la BERS)

10 100

mjet [GeV]

0.01

0.1

1

z

2-bodyphasespace

cut on z

keep

reject

0

0.5

1

1.5

2

2.5

3

3.5

10 100

1/N

dN

/dlo

g(m

jet)

mjet [GeV]

W+jet Jet Mass distributionPythia 6.4, pp!Wj, no UE

anti-kt, R=0.7

LHC, 7 TeV

pt,jets > 700 GeV

10 100

mjet [GeV]

0.01

0.1

1

z

2-bodyphasespace

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

Inside the jet mass

17

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10 100

1/N

dN

/dlo

g(m

jet)

mjet [GeV]

QCD Jet Mass distributionPythia 6.4, qq!qq, no UE

anti-kt, R=0.7

LHC, 7 TeV

pt,jets > 700 GeV

after cut on z > 0.25

(a la BERS)

10 100

mjet [GeV]

0.01

0.1

1

z

2-bodyphasespace

cut on z

keep

reject

0

0.5

1

1.5

2

2.5

3

3.5

10 100

1/N

dN

/dlo

g(m

jet)

mjet [GeV]

W+jet Jet Mass distributionPythia 6.4, pp!Wj, no UE

anti-kt, R=0.7

LHC, 7 TeV

pt,jets > 700 GeV

after cut on z > 0.25

10 100

mjet [GeV]

0.01

0.1

1

z

2-bodyphasespace

cut on z

keep

reject

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013 18

m/N

dN

/dm

jet

mjet [GeV]

qq ! qq + Wj mixture

pt,jets > 700 GeV

anti-kt, R = 0.7

0

0.05

0.1

0.15

0.2

0.25

0 50 100 150 200

+ CA subjet (z > 0.25)

Signal + bkgd after cut on z

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013 19

One core idea for grooming

[see blackboard]

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

“Grooming”

20

Noise removal from jets[1 jet ! 2 partons]

[Some of the other ideas]

[Boost 2010

writeup]

! Filtering Butterworth et al ’08

! Pruning Ellis, Vermillion and Walsh ’09

! Trimming Krohn, Thaler & Wang ’09

[With earlier methods by Seymour ’93 and Kodolova et al ’07;

Rubin ’10 for filtering optimisation; also Soper & Spannowsky ’10, ’11]

Jets 2 (M. Cacciari and G. Salam) GGI September 2011 24 / 29

Plain jet mass(anti-kt)

Groomed jetmasses

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013 21

How do the tools workin practice?

Identifying jet substructure: try out anti-kt

anti-kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

Identifying jet substructure: try out anti-kt

anti-kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

dmin is dij = 3.57137e−05

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

Identifying jet substructure: try out anti-kt

anti-kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

Identifying jet substructure: try out anti-kt

anti-kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

dmin is dij = 0.000496598

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

Identifying jet substructure: try out anti-kt

anti-kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

Identifying jet substructure: try out anti-kt

anti-kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

dmin is dij = 0.000688842

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

Identifying jet substructure: try out anti-kt

anti-kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

Identifying jet substructure: try out anti-kt

anti-kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

dmin is dij = 0.000805103

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

Anti-kt gradually makes its way

through the secondary blob ! no

clear identification of substructure

associated with 2nd parton.

Identifying jet substructure: try out anti-kt

anti-kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

Anti-kt gradually makes its way

through the secondary blob ! no

clear identification of substructure

associated with 2nd parton.

Identifying jet substructure: try out anti-kt

anti-kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

dmin is dij = 0.000773759

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

Anti-kt gradually makes its way

through the secondary blob ! no

clear identification of substructure

associated with 2nd parton.

Identifying jet substructure: try out anti-kt

anti-kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

Anti-kt gradually makes its way

through the secondary blob ! no

clear identification of substructure

associated with 2nd parton.

Identifying jet substructure: try out anti-kt

anti-kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

dmin is dij = 0.0014577

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

Anti-kt gradually makes its way

through the secondary blob ! no

clear identification of substructure

associated with 2nd parton.

Identifying jet substructure: try out anti-kt

anti-kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

Anti-kt gradually makes its way

through the secondary blob ! no

clear identification of substructure

associated with 2nd parton.

Identifying jet substructure: try out anti-kt

anti-kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

dmin is diB = 0.00147749

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

Anti-kt gradually makes its way

through the secondary blob ! no

clear identification of substructure

associated with 2nd parton.

Identifying jet substructure: try out anti-kt

anti-kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

Anti-kt gradually makes its way

through the secondary blob ! no

clear identification of substructure

associated with 2nd parton.

Identifying jet substructure: try out anti-kt

anti-kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

dmin is diB = 1.96

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

Anti-kt gradually makes its way

through the secondary blob ! no

clear identification of substructure

associated with 2nd parton.

Identifying jet substructure: try out anti-kt

anti-kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

Anti-kt gradually makes its way

through the secondary blob ! no

clear identification of substructure

associated with 2nd parton.

Identifying jet substructure: try out kt

kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

Identifying jet substructure: try out kt

kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

dmin is dij = 0.318802

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

Identifying jet substructure: try out kt

kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

Identifying jet substructure: try out kt

kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

dmin is dij = 0.977453

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

Identifying jet substructure: try out kt

kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

Identifying jet substructure: try out kt

kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

dmin is dij = 1.48276

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

kt clusters soft “junk” early on in theclustering

Identifying jet substructure: try out kt

kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

kt clusters soft “junk” early on in theclustering

Identifying jet substructure: try out kt

kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

dmin is dij = 2.34277

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

kt clusters soft “junk” early on in theclustering

Identifying jet substructure: try out kt

kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

kt clusters soft “junk” early on in theclustering

Identifying jet substructure: try out kt

kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

dmin is dij = 13.5981

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

kt clusters soft “junk” early on in theclustering

Identifying jet substructure: try out kt

kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

kt clusters soft “junk” early on in theclustering

Identifying jet substructure: try out kt

kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

dmin is dij = 30.8068

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

kt clusters soft “junk” early on in theclustering

Identifying jet substructure: try out kt

kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

kt clusters soft “junk” early on in theclustering

Identifying jet substructure: try out kt

kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

dmin is dij = 717.825

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

kt clusters soft “junk” early on in theclustering

Its last step is to merge two hard

pieces. Easily undone to identify un-

derlying kinematics

Identifying jet substructure: try out kt

kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

kt clusters soft “junk” early on in theclustering

Its last step is to merge two hard

pieces. Easily undone to identify un-

derlying kinematics

Identifying jet substructure: try out kt

kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

dmin is diB = 11432

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

kt clusters soft “junk” early on in theclustering

Its last step is to merge two hard

pieces. Easily undone to identify un-

derlying kinematics

Identifying jet substructure: try out kt

kt algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

This is crucial for identifying thekinematic variables of the partons inthe jet (e.g. z).

kt clusters soft “junk” early on in theclustering

Its last step is to merge two hard

pieces. Easily undone to identify un-

derlying kinematics

This meant it was the first algorithmto be used for jet substructure.

Seymour ’93

Butterworth, Cox & Forshaw ’02

Identifying jet substructure: Cam/Aachen

Cambridge/Aachen algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1

Identifying jet substructure: Cam/Aachen

Cambridge/Aachen algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

DeltaR_{ij} = 0.142857

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1

Identifying jet substructure: Cam/Aachen

Cambridge/Aachen algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1

Identifying jet substructure: Cam/Aachen

Cambridge/Aachen algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

DeltaR_{ij} = 0.214286

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1

Identifying jet substructure: Cam/Aachen

Cambridge/Aachen algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1

Identifying jet substructure: Cam/Aachen

Cambridge/Aachen algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

DeltaR_{ij} = 0.415037

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1

Identifying jet substructure: Cam/Aachen

Cambridge/Aachen algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1

Identifying jet substructure: Cam/Aachen

Cambridge/Aachen algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

DeltaR_{ij} = 0.686928

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1

Identifying jet substructure: Cam/Aachen

Cambridge/Aachen algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

C/A identifies two hard blobs withlimited soft contamination

G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1

Identifying jet substructure: Cam/Aachen

Cambridge/Aachen algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

DeltaR_{ij} = 1.20645

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

C/A identifies two hard blobs withlimited soft contamination, joinsthem

G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1

Identifying jet substructure: Cam/Aachen

Cambridge/Aachen algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

C/A identifies two hard blobs withlimited soft contamination, joinsthem

G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1

Identifying jet substructure: Cam/Aachen

Cambridge/Aachen algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

DeltaR_{ij} = 1.93202

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

C/A identifies two hard blobs withlimited soft contamination, joinsthem, and then adds in remainingsoft junk

G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1

Identifying jet substructure: Cam/Aachen

Cambridge/Aachen algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

C/A identifies two hard blobs withlimited soft contamination, joinsthem, and then adds in remainingsoft junk

G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1

Identifying jet substructure: Cam/Aachen

Cambridge/Aachen algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

DeltaR_{ij} > 2

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

C/A identifies two hard blobs withlimited soft contamination, joinsthem, and then adds in remainingsoft junk

G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1

Identifying jet substructure: Cam/Aachen

Cambridge/Aachen algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

C/A identifies two hard blobs withlimited soft contamination, joinsthem, and then adds in remainingsoft junk

G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1

Identifying jet substructure: Cam/Aachen

Cambridge/Aachen algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

DeltaR_{ij} > 2

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

C/A identifies two hard blobs withlimited soft contamination, joinsthem, and then adds in remainingsoft junk

G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1

Identifying jet substructure: Cam/Aachen

Cambridge/Aachen algorithm

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

How well can an algorithm identifythe “blobs” of energy inside a jet thatcome from di!erent partons?

C/A identifies two hard blobs withlimited soft contamination, joinsthem, and then adds in remainingsoft junk

The interesting substructure is buriedinside the clustering sequence — it’sless contamined by soft junk, butneeds to be pulled out with specialtechniques

Butterworth, Davison, Rubin & GPS ’08Kaplan, Schwartz, Reherman & Tweedie ’08

Butterworth, Ellis, Rubin & GPS ’09Ellis, Vermilion & Walsh ’09

G. Salam (CERN/Princeton/Paris) test-bed., G. Salam June 13, 2013 1 / 1

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

73

anti-kt algorithmpt/GeV

50

40

20

00 1 2 3 4 y

30

10

kt algorithmpt/GeV

50

40

20

00 1 2 3 4 y

30

10

Cambridge/Aachen

pt/GeV

50

40

20

00 1 2 3 4 y

30

10

Jets lecture 3 (Gavin Salam) CERN Academic Training March/April 2011 19 / 29

pp ! ZH ! !!̄bb̄, @14TeV, mH=115GeV

Herwig 6.510 + Jimmy 4.31 + FastJet 2.3

Cluster event, C/A, R=1.2

Butterworth, Davison, Rubin & GPS ’08

SIGNAL

Zbb BACKGROUND

arbitrary norm.

pp ! ZH ! !!̄bb̄, @14TeV, mH=115GeV

Herwig 6.510 + Jimmy 4.31 + FastJet 2.3

Fill it in, ! show jets more clearly

Butterworth, Davison, Rubin & GPS ’08

SIGNAL

Zbb BACKGROUND

arbitrary norm.

pp ! ZH ! !!̄bb̄, @14TeV, mH=115GeV

Herwig 6.510 + Jimmy 4.31 + FastJet 2.3

Consider hardest jet, m = 150 GeV

Butterworth, Davison, Rubin & GPS ’08

SIGNAL

0

0.05

0.1

0.15

80 100 120 140 160mH [GeV]

200 < ptZ < 250 GeV

Zbb BACKGROUND

0

0.002

0.004

0.006

0.008

80 100 120 140 160mH [GeV]

200 < ptZ < 250 GeV

arbitrary norm.

pp ! ZH ! !!̄bb̄, @14TeV, mH=115GeV

Herwig 6.510 + Jimmy 4.31 + FastJet 2.3

split: m = 150 GeV, max(m1,m2)m

= 0.92 ! repeat

Butterworth, Davison, Rubin & GPS ’08

SIGNAL

0

0.05

0.1

0.15

80 100 120 140 160mH [GeV]

200 < ptZ < 250 GeV

Zbb BACKGROUND

0

0.002

0.004

0.006

0.008

80 100 120 140 160mH [GeV]

200 < ptZ < 250 GeV

arbitrary norm.

pp ! ZH ! !!̄bb̄, @14TeV, mH=115GeV

Herwig 6.510 + Jimmy 4.31 + FastJet 2.3

split: m = 139 GeV, max(m1,m2)m

= 0.37 ! mass drop

Butterworth, Davison, Rubin & GPS ’08

SIGNAL

0

0.05

0.1

0.15

80 100 120 140 160mH [GeV]

200 < ptZ < 250 GeV

Zbb BACKGROUND

0

0.002

0.004

0.006

0.008

80 100 120 140 160mH [GeV]

200 < ptZ < 250 GeV

arbitrary norm.

pp ! ZH ! !!̄bb̄, @14TeV, mH=115GeV

Herwig 6.510 + Jimmy 4.31 + FastJet 2.3

check: y12 "pt2pt1

" 0.7 ! OK + 2 b-tags (anti-QCD)

Butterworth, Davison, Rubin & GPS ’08

SIGNAL

0

0.05

0.1

0.15

80 100 120 140 160mH [GeV]

200 < ptZ < 250 GeV

Zbb BACKGROUND

0

0.002

0.004

0.006

0.008

80 100 120 140 160mH [GeV]

200 < ptZ < 250 GeV

arbitrary norm.

pp ! ZH ! !!̄bb̄, @14TeV, mH=115GeV

Herwig 6.510 + Jimmy 4.31 + FastJet 2.3

Rfilt = 0.3

Butterworth, Davison, Rubin & GPS ’08

SIGNAL

0

0.05

0.1

0.15

80 100 120 140 160mH [GeV]

200 < ptZ < 250 GeV

Zbb BACKGROUND

0

0.002

0.004

0.006

0.008

80 100 120 140 160mH [GeV]

200 < ptZ < 250 GeV

arbitrary norm.

pp ! ZH ! !!̄bb̄, @14TeV, mH=115GeV

Herwig 6.510 + Jimmy 4.31 + FastJet 2.3

Rfilt = 0.3: take 3 hardest, m = 117 GeV

Butterworth, Davison, Rubin & GPS ’08

SIGNAL

0

0.05

0.1

0.15

80 100 120 140 160mH [GeV]

200 < ptZ < 250 GeV

Zbb BACKGROUND

0

0.002

0.004

0.006

0.008

80 100 120 140 160mH [GeV]

200 < ptZ < 250 GeV

arbitrary norm.

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

Boosted Higgs analysis

82

Cluster with a large RUndo the clustering into subjets,

until a large mass drop is observed

Re-cluster with smaller R, and keep only 3

hardest jets

pp →ZH → ννbb--

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013 83

#include “fastjet/tools/MassDropTagger.hh”#include “fastjet/tools/Filter.hh”

JetDefinition jet_def(cambridge_algorithm, 1.2);ClusterSequence cs(input_particles, jet_def);jets = sorted_by_pt(cs.inclusive_jets());

// define the tagger and use itdouble mu = 0.667, ycut = 0.09;MassDropTagger md_tagger(mu, ycut);PseudoJet tagged = md_tagger(jets[0]);// check it was tagged OK by verifying (tagged != 0)

// define the filter and use itFilter filter(0.3,SelectorNHardest(3));PseudoJet filtered = filter(tagged); // this is the Higgs!!

Mass-Drop Tagger + Filtering in FastJet

The real analysis is slightly more refined (b-tagging, dynamical filter radius, etc) but the main features are already present here

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

Pruning

84

0

0.05

0.1

0.15

0.2

0.25

0.3

0.001 0.01 0.1 1

10 100 1000

m/σ

dσ

/ dm

m/pt

Monte Carlo

m [GeV], for pt = 4 TeV

mMDT, ycut = 0.03mMDT, ycut = 0.13

0

0.05

0.1

0.15

0.2

0.25

0.3

0.001 0.01 0.1 1

10 100 1000

m/σ

dσ

/ dm

m/pt

Analytic Calculation

m [GeV], for pt = 4 TeV

mMDT, ycut = 0.03mMDT, ycut = 0.13mMDT, ycut = 0.13 some finite ycut

Figure 4: Comparison of Monte Carlo and analytic results for the mMDT tagged mass spectrumfor two values of ycut. In all cases we have used µ = 0.67.

We compare our analytical predictions with the Monte Carlo simulation results in

Fig. 4 for several ycut values. There is acceptable agreement — remaining di!erences

are attributable to finite ycut e!ects and di!erences beyond single-log accuracy between

our treatment here and that in the Monte Carlo (e.g. subleading terms in the running

coupling). Furthermore, the pattern of ycut dependence, with smaller values of ycut leading

to an increase in the mass spectrum at moderate ! values and a steeper fall-o! towards

lower ! values, in line with the pattern visible in the second-order expansion of Eq. (5.4).

6. Pruning

Pruning [7, 8] takes an initial jet, and from its mass deduces a pruning radius Rprune =

Rfact · 2mpt

, where Rfact is a parameter of the tagger. It then reclusters the jet and for

every clustering step, involving objects a and b, it checks whether "ab > Rprune and

min(pta, ptb) < zcutpt,(a+b), where zcut is a second parameter of the tagger. If so, then the

softer of the a and b is discarded. Otherwise a and b are recombined as usual. Clustering

then proceeds with the remaining objects, applying the pruning check at each stage.

In analysing pruning, we will take Rfact =12 , i.e. its default suggested value. In analogy

with our approach for the ycut parameter in the (m)MDT, we will work in the limit of small

zcut (but ln zcut not too large). And we will assume that the reclustering is performed with

the Cambridge/Aachen algorithm, the most common choice.

At leading order, i.e. a jet involving a single 1 ! 2 splitting, then Rprune = mpt

=

"ab

!

z(1" z), which guarantees that "ab is always larger than Rprune. To establish the

pruned jet mass, one then needs to examine the second part of the pruning condition: if

min(z, 1 " z) > zcut then the clustering is accepted and the pruned jet has a finite mass.

Otherwise the pruned jet mass is zero. This pattern is true independently of the angle

between the two prongs. Thus, as for the MD tagger, pruning removes the divergent low-z

region from the jet-mass phasespace. As a result, in the small zcut limit, one obtains the

same LO result for the pruned mass distribution as for the MD tagger case (replacing

– 13 –

#include “fastjet/tools/Pruner.hh”

// define prunerdouble zcut = 0.1, Rfact = 0.5;Pruner pruner(cambridge_algorithm, zcut, Rfact);

PseudoJet pruned_jet = pruner(jet);

Ellis, Vermilion and Walsh ’09

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

Trimming

85

Different condition for retaining jets (pT-cut rather than nfilt hardest)

with respect to filtering, but otherwise identical

#include “fastjet/tools/Filter.hh”

// define trimmerFilter trimmer(0.3,SelectorPtFractionMin(0.03));

Krohn, Thaler & Wang ’09

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

different (2-body) substructure tools

86

Gro

omin

g

Tagging

jet mass

N-subjettiness

filteringtrimming

pruning

MDT

Detailed relative positions depend on physics context(and are possibly contentious!)

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

different (2-body) substructure tools

86

Gro

omin

g

Tagging

jet mass

N-subjettiness

filteringtrimming

pruning

MDT

combined

Detailed relative positions depend on physics context(and are possibly contentious!)

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

different (2-body) substructure tools

86

Gro

omin

g

Tagging

jet mass

N-subjettiness

filteringtrimming

pruning

MDT

combined

multivariatetaggers

Detailed relative positions depend on physics context(and are possibly contentious!)

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

different (2-body) substructure tools

86

Gro

omin

g

Tagging

jet mass

N-subjettiness

filteringtrimming

pruning

MDT

combined

multivariatetaggers

Detailed relative positions depend on physics context(and are possibly contentious!)

template tagger

jet deconstruction

Q-jets

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

3rd party tools for FastJet

87

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

Comparing top taggers

88

Comparing top taggers: QCD fakes rate v. signal e!.[Methods]

Herwig, 500 < pt < 600 GeV Herwig++, 200 < pt < 800 GeV

From the extensive “Boost 2011” report, which reviewed taggersdiscussed software, determined performance on MC, etc.

Bottom line: some taggers clearly better than others.But many taggers behave similarly & details depend on analysis

(+ MC choice)

Gavin Salam (CERN/Princeton/CNRS) Theory of Fat Jets Higgs Hunting 2012-07-19 16 / 28

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

Boosted Ws and tops in single jets: data!

W’s in a single jet

with Pruning + Mass Drop requirement

NB: combined in IR unsafe way. . .

tops in a single jet

with HEPTopTaggerGavin Salam (CERN) Perturbative QCD in hadron collisions SILAFAE 2012-12-10 32 / 35

Seeing W’s and tops in a single jet

89

CMS single-jet W mass peakin events with a lepton andseparate b-tagged jet.

Uses pruning (+ mass-dropcondition on split jet)

Gavin Salam (CERN/Princeton/CNRS) Theory of Fat Jets Higgs Hunting 2012-07-19 19 / 28

Leading Fat Jet Mass [GeV]100 200 300 400 500 600

Even

ts /

20 G

eV

0

20

40

60

80

100

120

140

160

180 Data 2011

= 1.3 pbσZ' (1 TeV)

ttMultijet

ATLAS-1 L dt = 4.7 fb∫

= 7 TeVs

(a)

Leading Top-Quark Candidate Mass [GeV]140 150 160 170 180 190 200 210

Even

ts /

2 G

eV0

20

40

60

80

100

120Data 2011

= 1.3 pbσZ' (1 TeV)

ttMultijet

ATLAS-1 L dt = 4.7 fb∫

= 7 TeVs

(b)

Figure 7. Signal region distributions of (a) the mass of the leading pT fat jet and (b) the massof the leading pT top-quark candidate. Also shown are the prediction for SM tt̄ production, themultijet background contribution as estimated from data, and a hypothetical Z 0 boson signal.

– 16 –

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

Searches with substructure tools

90

Some BSM searches with jet-substructure techniques

A range of techniques being used for varied BSM scenarios

Gavin Salam (CERN/LPTHE/Princeton) Jets in Higgs Searches HC2012 2012-11-18 24 / 29

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013 92

EXTRA MATERIAL

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

What do we know currently?

At the time:• No clear picture of why the taggers might be similar or

different• No clear picture of how the parameter choices affect

the taggers

93

Boost 2010 proceedings:

1. Introduction

The Large Hadron Collider (LHC) at CERN is increasingly exploring phenomena at ener-

gies far above the electroweak scale. One of the features of this exploration is that analysis

techniques developed for earlier colliders, in which electroweak-scale particles could be con-

sidered “heavy”, have to be fundamentally reconsidered at the LHC. In particular, in the

context of jet-related studies, the large boost of electroweak bosons and top quarks causes

their hadronic decays to become collimated inside a single jet. Consequently a vibrant

research field has emerged in recent years, investigating how best to tag the characteristic

substructure that appears inside the single “fat” jets from electroweak scale objects, as

reviewed in Refs. [?,?,26]. In parallel, the methods that have been developed have started

to be tested and applied in numerous experimental analyses (e.g. [23–25] for studies on

QCD jets and [some searches]).

The taggers’ action is twofold: they aim to suppress or reshape backgrounds, while re-

taining signal jets and enhancing their characteristic jet-mass peak at the W/Z/H/top/etc.

mass. Nearly all the discussion of these aspects has taken place in the context of Monte

Carlo simulation studies [Some list], with tools such as Herwig [?, ?], Pythia [?, ?] and

Sherpa [?]. While Monte Carlo simulation is an extremely powerful tool, its intrinsic nu-

merical nature can make it di!cult to extract the key characteristics of individual taggers

and the relations between taggers (examining appropriate variables, as in [4], can be helpful

in this respect). As an example of the kind of statements that exist about them in the

literature, we quote from the Boost 2010 proceedings:

The [Monte Carlo] findings discussed above indicate that while [pruning,

trimming and filtering] have qualitatively similar e"ects, there are important

di"erences. For our choice of parameters, pruning acts most aggressively on the

signal and background followed by trimming and filtering.

While true, this brings no insight about whether the di"erences are due to intrinsic proper-

ties of the taggers or instead due to the particular parameters that were chosen; nor does it

allow one to understand whether any di"erences are generic, or restricted to some specific

kinematic range, e.g. in jet transverse momentum. Furthermore there can be significant

di"erences between Monte Carlo simulation tools (see e.g. [22]), which may be hard to diag-

nose experimentally, because of the many kinds of physics e"ect that contribute to the jet

structure (final-state showering, initial-state showering, underlying event, hadronisation,

etc.). Overall, this points to a need to carry out analytical calculations to understand the

interplay between the taggers and the quantum chromodynamical (QCD) showering that

occurs in both signal and background jets.

So far there have been three investigations into the analytical features that emerge from

substructure taggers. Ref. [19, 20] investigated the mass resolution that can be obtained

on signal jets and how to optimize the parameters of a method known as filtering [1].

Ref. [13] discussed constraints that might arise if one is to apply Soft Collinear E"ective

Theory (SCET) to jet substructure calculations. Ref. [14] observed that for narrow jets the

distribution of the N -subjettiness shape variable for 2-body signal decays can be resummed

– 2 –

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 201394

0

0.1

0.2

0.3

0.4

0.5

0.6

0.001 0.01 0.1 1

10 100 1000

m/m

dm

/ dm

m/pt

m [GeV], for pt = 4 TeV

Pythia 6 DW

, parton-shower level, no U

E, pp 14 TeV, pt,gen > 4 TeV, qq A qq, R

= 1

plain jet mass

The “right” MC study can already be instructive(testing on background [quark] jets)

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 201395

0

0.1

0.2

0.3

0.4

0.5

0.6

0.001 0.01 0.1 1

10 100 1000

m/m

dm

/ dm

m/pt

m [GeV], for pt = 4 TeV

Pythia 6 DW

, parton-shower level, no U

E, pp 14 TeV, pt,gen > 4 TeV, qq A qq, R

= 1

plain jet massMass-drop tagger (ycut=0.09, µ=0.67)

Pruner (zcut=0.1)

Trimmer (zcut=0.1, Rtrim=0.2) Different taggersare apparentlyquite similar

The “right” MC study can already be instructive(testing on background [quark] jets)

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 201396

0

0.1

0.2

0.3

0.4

0.5

0.6

0.001 0.01 0.1 1

10 100 1000

m/m

dm

/ dm

m/pt

m [GeV], for pt = 4 TeV

Pythia 6 DW

, parton-shower level, no U

E, pp 14 TeV, pt,gen > 4 TeV, qq A qq, R

= 1

plain jet massMass-drop tagger (ycut=0.09, µ=0.67)

Pruner (zcut=0.1)

Trimmer (zcut=0.1, Rtrim=0.2) But only for a limited range

of masses

The “right” MC study can already be instructive(testing on background [quark] jets)

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

Pileup

97

Jet

perturbative radiation

non-perturbative phase (hadronisation)

background radiation (underlying event)

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

Pileup

97

Jet

perturbative radiation

non-perturbative phase (hadronisation)

background radiation (underlying event)

background radiation (pileup)

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

Pileup

97

Jet

perturbative radiation

non-perturbative phase (hadronisation)

background radiation (underlying event)

background radiation (pileup)

Out of time pileup(especially ATLAS)

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

Effect of pileup on 2 TeV Z’

98

1/N

dn/

dbin

/ 2

dijet mass [GeV]

qq, M = 2000 GeV

arXiv:0810.1304 0

0.02

0.04

0.06

0.08

1900 2000 2100

anti-kt, R=0.7Qw

f=0.12 = 26.1 GeV

1/N

dn/

dbin

/ 2

dijet mass [GeV]

qq, M = 2000 GeV

arXiv:0810.1304

0

0.02

0.04

0.06

0.08

1900 2000 2100

anti-kt, R=0.7Qw

f=0.12 = 51.9 GeVPU = 0.25 mb-1

no pileup ~25 pileup

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013 99

ATLAS

Pileup for real

a few cm

~ 20

m

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

What goes into the jets?

100Figure: There are also magnets and things.

I Current grooming techniques only use calorimeter information.

I Particles tracks automatically remove pileup.

I Can tracking information be used to improve grooming?

Matthew Low (UChicago) Supercharged Jet Grooming June 12, 2013 6 / 11

ATLASCalorimeter towers, afterpre-clustering them into

“topoclusters”

Figure

: Ther

e are a

lsoma

gnets a

ndthin

gs.

I Curren

t groom

ingtec

hnique

s only

usecalo

rimete

r inform

ation.

I Particl

es trac

ksaut

omatic

allyrem

ovepile

up.

I Cantrac

king in

formatio

n beuse

d to imp

rove g

roomin

g?

M

a

t

t

h

e

w

L

o

w

(

U

C

h

i

c

a

g

o

)

S

u

p

e

r

c

h

a

r

g

e

d

J

e

t

G

r

o

o

m

i

n

g

J

u

n

e

1

2

,

2

0

1

3

6

/

1

1

CMS“Particle flow” objects ~

1) charged tracks

2) neutrals: calorimeter towers not associated with

charged tracks(or leftover bits of calo if

Ecalo – Etrack ≫ √Ecalo )

Matthew Low’s ideal detector

A CMS particle-flowexpert would shudder

at this description

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

What goes into the jets?

100Figure: There are also magnets and things.

I Current grooming techniques only use calorimeter information.

I Particles tracks automatically remove pileup.

I Can tracking information be used to improve grooming?

Matthew Low (UChicago) Supercharged Jet Grooming June 12, 2013 6 / 11

ATLASCalorimeter towers, afterpre-clustering them into

“topoclusters”

Figure

: Ther

e are a

lsoma

gnets a

ndthin

gs.

I Curren

t groom

ingtec

hnique

s only

usecalo

rimete

r inform

ation.

I Particl

es trac

ksaut

omatic

allyrem

ovepile

up.

I Cantrac

king in

formatio

n beuse

d to imp

rove g

roomin

g?

M

a

t

t

h

e

w

L

o

w

(

U

C

h

i

c

a

g

o

)

S

u

p

e

r

c

h

a

r

g

e

d

J

e

t

G

r

o

o

m

i

n

g

J

u

n

e

1

2

,

2

0

1

3

6

/

1

1

CMS“Particle flow” objects ~

1) charged tracks

2) neutrals: calorimeter towers not associated with

charged tracks(or leftover bits of calo if

Ecalo – Etrack ≫ √Ecalo )

Matthew Low’s ideal detector

A CMS particle-flowexpert would shudder

at this description

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

How do you remove pileup (PU)?

101

2. CMS

Throw out charged tracks not from the main primary

vertex

Use area/median(FastJet)method for neutral PU

1. Offset method

Count # of pileup vertices (npileup):

c ~ 0.5 GeV

3. Area/median (FastJet) method

Determine density of pileup pt / unit area ≡ ρ

psubtractedt,jet = pt,jet � ⇢⇥Ajet

psubtractedt,jet = pt,jet � c⇥ npileup

[Tevatron / old ATLAS]

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013 102

Add “ghosts”, infinitesimally soft particles, to track “area”

of jet in y–φ plane

yφ

pt

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013 102

Add “ghosts”, infinitesimally soft particles, to track “area”

of jet in y–φ plane

yφ

pt

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013 102

Add “ghosts”, infinitesimally soft particles, to track “area”

of jet in y–φ plane

yφ

pt

0

50

100

150

200

250

-4 -3 -2 -1 0 1 2 3 4

ptj / A

j [G

eV

]

yj

kt algorithm, R=0.5

Most jets are from pileup; all have similar pt / area

Estimate ρ for a given event as

⇢ = medianjets

⇢pt,jetAjet

�

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

pileup subtraction performance

103

I Can subtract pT based ondensity of event [Cacciari,Salam; arxiv:0707.1378]

p

(sub)

T = pT � ⇢A

⇢ = median

⇢pT ,j

Aj

�� 0

0.005

0.01

0.015

1900 2000 2100

1/N

dN

/dm

[G

eV

-1]

m [GeV]

kt, R=0.7

LHC, high lumiZ! at 2 TeV

no pileup

no pileup, sub

pileup

pileup, sub

I Jet shapes can also be subtracted [Soyez, Salam, Kim, Duta, Cacciari;arxiv:1211.280110]

V

jet,sub

= V

jet

� ⇢V [1]

jet

+1

2⇢2V [2]

jet

+ . . .

Matthew Low (UChicago) Supercharged Jet Grooming June 12, 2013 5 / 11

Used in CMS for neutral part of PU, and (I think) for recent ATLAS results

Gavin Salam (CERN) Jets and jet substructure (4) TASI, June 2013

Some observables particularly sensitive to PU

104

Beyond simply providing a pile-up-independent average jet mass, the optimal grooming configura-tions render the full jet mass spectrum insensitive to high instantaneous luminosity. Figure 3 demon-strates this by comparing the jet mass spectrum for leading ungroomed and trimmed anti-kt jets. Thecomparison is performed both in data and using the Z0 ! tt̄ MC sample. The result using the inclusivejet sample obtained from data shows that a nearly identical trimmed mjet spectrum is obtained regardlessof the level of pile-up. The peak of the leading jet mass distribution for events with NPV � 12 is shiftedcomparatively more due to trimming: from mjet ⇡ 125 GeV to mjet ⇡ 45 GeV as compared to an initialpeak position of mjet ⇡ 90 GeV for events with 1 NPV 4. Nonetheless, the resulting trimmed jetmass spectra exhibit no dependence on NPV.

1jetLeading jet mass, m

0 50 100 150 200 250 300

Arbi

trary

uni

ts

0

0.02

0.04

0.06

0.08

0.1

ATLAS Preliminary-1 Ldt = 4.7 fb∫Data 2011,

with R=1.0 LCW, No jet groomingtanti-k| < 0.8η < 800 GeV, |

Tjet p≤600

4≤PVN≤17≤PVN≤511≤PVN≤8

12≥PVN

(a) Data: anti-kt, R = 1.0: Ungroomed1jetLeading jet mass, m

0 50 100 150 200 250 300

Arbi

trary

uni

ts

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14 ATLAS Preliminary-1 Ldt = 4.7 fb∫Data 2011,

=0.3sub=0.05, Rcut with R=1.0 LCW, ftanti-k| < 0.8η < 800 GeV, |

T

jet p≤600 4≤PVN≤17≤PVN≤511≤PVN≤8

12≥PVN

(b) Data: anti-kt, R = 1.0: Trimmed

1jetLeading jet mass, m

120 140 160 180 200 220 240 260

Arbi

trary

uni

ts

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18 ATLAS Preliminary - Simulation= 1.6 TeV)

Z' (mt t→Z' with R=1.0 LCW, No jet groomingtanti-k

| < 0.8η < 800 GeV, |Tjet p≤600

4≤PVN≤17≤PVN≤511≤PVN≤8

12≥PVN

(c) Z0: anti-kt, R = 1.0: Ungroomed1jetLeading jet mass, m

120 140 160 180 200 220 240 260

Arbi

trary

uni

ts

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18 ATLAS Preliminary - Simulation= 1.6 TeV)

Z' (mt t→Z'

=0.3sub=0.05, Rcut with R=1.0 LCW, ftanti-k| < 0.8η < 800 GeV, |

Tjet p≤600

4≤PVN≤17≤PVN≤511≤PVN≤8

12≥PVN

(d) Z0: anti-kt, R = 1.0: Trimmed

Figure 3: Jet mass spectra for four primary vertex multiplicity ranges for anti-kt jets with R = 1.0 inthe range 600 pjet

T < 800 GeV. Both untrimmed anti-kt jets (left) and trimmed anti-kt jets (right) arecompared for the various NPV ranges in data (top) and for a Z0 ! tt̄ sample (bottom).

8

E.g. jet mass

For these, plain pileup subtraction

is only OKish

But why are weinterested in the

jet mass?