Water Vapour Radiometry at ALMA: Properties of the...
Transcript of Water Vapour Radiometry at ALMA: Properties of the...
Water Vapour Radiometry at ALMA: Properties ofthe Atmosphere and First Tests of Phase Correction
B. Nikolic, R. Bolton & J. S. Richer
Cavendish Laboratory/Kavli InstituteUniversity of Cambridge
8 March 2011
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Atacama Large Millimetre Array (ALMA)Artist’s vision in 2006
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ALMA AOS in October 2010from http://wikis.alma.cl/bin/view/AIV/CommissioningStatusReview
Photo: N. Mizuno
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ALMA AOS
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ALMA Transporter in action
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ALMA 7 m antenna
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ALMA test images released by JAO
• This shows the well-known spiral NGC253, with an optical image of the whole galaxy on the left (credit: ESO). The ALMA test images show dense clouds of gas in the central regions of the galaxy: (middle) the CO J = 2-1 line at 230 GHz and (right) the continuum and CO J = 6-5 line at 690 GHz. The small ellipse in the bottom left hand corner of each of the ALMA test images indicates the angular resolution – that shows the size of the beam formed when the signals from the antennas are combined together. There were six antennas for the 230 GHz observations and only four for those at 690 GHz.
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ALMA test images released by JAO
• An example of ALMA’s potential as a spectroscopic instrument: on the left is the map of the molecular “hot core” G34.26+0.15, which is unresolved with the short baselines that we are presently using, so the “image” is not very interesting whereas a section of the spectrum near 100 GHz shows a “forest” of molecular lines. A few of the chemical species that are responsible for the emission lines are identified on the plot.(University of Cambridge) WVR phase correction at ALMA March 2011 8 / 52
ALMA test images released by JAO
• This shows the emission from the disk of dust surrounding the star Beta Pictoris. On the left is an image at 70 microns wavelength made with Herschel, (Olofsson et al., SDP Presentations, Madrid, Dec 2009) and on the right is the ALMA test data at 870 microns showing the denser material in the central region of the disk. At this distance 5 arcseconds corresponds to 100 times the radius of the Earth’s orbit around the Sun, or about twice the radius of the “Kuiper belt” surrounding the Solar System, which contains many dwarf planets and also some dust, but much less than in the disk around Beta-Pic. The disk is very thin and we are viewing it edge on – in both observations the apparent thickness is a reflection of the angular resolution of the instrument.(University of Cambridge) WVR phase correction at ALMA March 2011 9 / 52
Introduction
Outline
1 Introduction
2 Phase correction software
3 First results at ALMAGood examplesPoor examplesEffect on the beam“Dry” fluctuations
4 Summary
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Introduction
Atmospheric Phase Fluctuations
R(t)
The turbulenttroposphere
Astronomicalwavefront
Corruptedastronomicalwavefront
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Introduction
Atmospheric Phase Fluctuations
R(t)
The turbulenttroposphere
Astronomicalwavefront
Corruptedastronomicalwavefront
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Introduction
Atmospheric Phase Fluctuations
R(t)
The turbulenttroposphere
Astronomicalwavefront
Corruptedastronomicalwavefront
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Introduction
Path fluctuations due to the atmosphere
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m)
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m)
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|A|
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Introduction
Phase closure (→ antenna based errors)Antenna 0 Vs 1 Antenna 0 Vs 2
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Introduction
Atmospheric transmission + ALMA bands1 mm precipitable water vapour; absorption is dominated by H2O, O2
Band 2
Band 3 Band 4
Band 5
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Band 7
Band 8 Band 9
Band 10
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Introduction
Water Vapour cm/mm/sub-mm lines1 mm precipitable water vapour
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(K)
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Introduction
Water Vapour cm/mm/sub-mm lines1 mm precipitable water vapour
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ν (GHz)ν (GHz)
22 GHz Water Line –previous WVR systems
183 GHz Water Line –ALMA WVR system
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Introduction
The 183 GHz Water Vapour LineBlue rectangles are the production WVR filters
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)T b
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Introduction
WVR in the ALMA receiver cabin
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Introduction
ALMA 183 GHz Water Vapour RadiometersTechnical concept of the units
To Relay Optics& The Sky
Chop
Cold Load
Hot Load
LO Chain
Input 125 MHz ref-erence signal
Digital synth Amplifier Freq. Multiplier
90 GHzLO Signal
LNA
0.5-2
2-4.5
4.5-6.5
6.5-8.5
Det
ectio
n,Sa
mpl
ing
&R
eado
ut
Readout viaCAN Bus
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Introduction
ALMA 183 GHz Water Vapour RadiometersTechnical concept of the units
Un-cooled mixer, double-sideband, with ≈ 1000 K receiver noiseTotal bandwidth ≈ 18 GHz split into four DSB channelsDicke-switched with a chopper wheel against loads at twotemperatures allowing continuous calibrationSpecifications:
Sensitivity: 0.08–0.1 K per channel RMSStability: 0.1 K peak-to-peak over 10 minutes + 10 degree tiltsAbsolute accuracy: 2 K maximum error
Hardware complete!All units delivered to ALMA
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Introduction
Sky brightness observed by WVRs
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)T B
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)T B
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Observed brightness tem-peratures of WVR on thethree antennas involved inthis test observation. Thefour colours in each panelare the four channels of theWVRs.
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Introduction
Correlation between WVR signals and path
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)∆T
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Introduction
Convert WVR signals into path fluctuation estimatesThis allows to correct the phase!
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Introduction
Raw & WVR corrected inferred path3-minute running mean removed to accentuate atmospheric effects
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resi
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Phase correction software
Outline
1 Introduction
2 Phase correction software
3 First results at ALMAGood examplesPoor examplesEffect on the beam“Dry” fluctuations
4 Summary
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Phase correction software
Stage I – Retrieval of atmospheric parameters
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)T B
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)∆T
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TB: Four brightnesstemperatures at
one instant in time
p(T,P,c): Priors
Water vapour modelNested samplingBayesian analysis
Atmospheric condi-tions: distributions
for P, T and c
Bayesian Evidence
dT/dL: Phase cor-rection coefficients
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Phase correction software
Stage II – Conversion to path fluctuation
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dT/dL: Phase cor-rection coefficients
TB: Four brightnesstemperatures as
function of time
Noise estimates
Linear trans-formation (or
something close)Path error toeach antenna
Error estimates
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Phase correction software
Stage III – Application to visibilities
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Observed visibilities
Path estimate
Other calibra-tion tables
CASA
Corrected visibilities
Map making,model fitting, etc
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Phase correction software
Software architecture
CASA dependent code Better I/OCore algorithms
LibAir package
GSL Boost CASA HDF5
BNMin1:inference /
optimisationLibAIR Core LibAIR I/O
for CASALibAIR I/Ofor HDF5
SWIGLibAIRPython
bindings
wvrgcal:main user-
facingapplication
msdump:Extract data
from MSto HDF5
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Phase correction software
wvrgcal – the ALMA/CASA phase correction applicationSee http://www.mrao.cam.ac.uk/˜bn204/alma/wvrsoft.html
This is the user-facing application that does WVR phasecorrectionFully compatible with official ALMA software:
Reads ALMA/CASA Measurement SetsWrites CASA Gain Calibration (“T”-Jones) tablesCallable and scriptable from CASAInter-operates with CASA facilities like applycal, accum,plotcal & browsetable
Built on top of:LibAIR, the phase correction libraryCASA-Core and CASA libraries for input/output
Public download, as source-code and Linux binariesWorks! – Now we are making it better
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Phase correction software
User documentationhttp://wikis.alma.cl/bin/view/AIV/Application
Also: mailing list hosted here in Cambridge: https://lists.cam.ac.uk/mailman/listinfo/mrao-wvrgcal
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First results at ALMA
Outline
1 Introduction
2 Phase correction software
3 First results at ALMAGood examplesPoor examplesEffect on the beam“Dry” fluctuations
4 Summary
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First results at ALMA Good examples
Outline
1 Introduction
2 Phase correction software
3 First results at ALMAGood examplesPoor examplesEffect on the beam“Dry” fluctuations
4 Summary
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First results at ALMA Good examples
First application – February 2010Courtesy of Al Wooten
02:41:16.8 02:52:48.0 03:04:19.2 03:15:50.4 03:27:21.6
Time
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of
Corr
ect
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ata
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eg)
This was an observation switching between two quasars. Blue: uncorrectedphase; red: corrected phase
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First results at ALMA Good examples
This morningCourtesy of Satoki Matsushita
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First results at ALMA Good examples
Data-set A002 Xb9f5d X1: Short baselineRed: uncorrected phase; Blue: corrected phase
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First results at ALMA Good examples
Data-set A002 Xb9f5d X1: Long baselineRed: uncorrected phase; Blue: corrected phase
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First results at ALMA Good examples
Data-set A002 Xb9fce X1Similar conditions, shortly after in time compared to the results in the previous slide
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First results at ALMA Good examples
Data-set A002 Xa0705 X1Another example, only short baselines
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First results at ALMA Poor examples
Outline
1 Introduction
2 Phase correction software
3 First results at ALMAGood examplesPoor examplesEffect on the beam“Dry” fluctuations
4 Summary
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First results at ALMA Poor examples
Data-set A002 Xba2ed X1Short baselines, leak-through phase fluctuations (offset in coefficients due totime-constant cloud?)
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Very short baseline (A0-A1), essentially no phase fluctuations tocorrect
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First results at ALMA Poor examples
Data-set A002 Xba2ed X1Short baselines, leak-through phase fluctuations (offset in coefficients due totime-constant cloud?)
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Also a very short baseline (A0-A2), some atmospheric-like phasefluctuation seen and corrected
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First results at ALMA Poor examples
Data-set A002 Xba2ed X1Short baselines, leak-through phase fluctuations (offset in coefficients due totime-constant cloud?)
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Slightly longer baseline (A0-A3): atmospheric phase fluctuationsclearly seen, corrected somewhat but clear “leak-through”
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First results at ALMA Poor examples
Data-set A002 Xba2ed X1Short baselines, leak-through phase fluctuations (offset in coefficients due totime-constant cloud?)
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rees
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Slightly longer baseline (A1-A3): again WVR correction helps but“leak-through”
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First results at ALMA Poor examples
Dataset A002 X9c46d X1Very good weather but dominated by instrumentals (BL A0-A1)
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First results at ALMA Poor examples
Dataset A002 X9c46d X1Very good weather but dominated by instrumentals (BL A0-A2)
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First results at ALMA Effect on the beam
Outline
1 Introduction
2 Phase correction software
3 First results at ALMAGood examplesPoor examplesEffect on the beam“Dry” fluctuations
4 Summary
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First results at ALMA Effect on the beam
Inner ALMA pad positionsFrom http://www.alma.cl/˜dbarkats/pad_position_plotter/plots/ALMA_pad_viewer_zoom2.html
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First results at ALMA Effect on the beam
Effect of WVR correction on the ALMA beam
No phase correction WVR phase correction
Short observation with very inhomogeneous uv distribution – one antennawas on a long north baseline and others were close together in a cluster
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First results at ALMA “Dry” fluctuations
Outline
1 Introduction
2 Phase correction software
3 First results at ALMAGood examplesPoor examplesEffect on the beam“Dry” fluctuations
4 Summary
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First results at ALMA “Dry” fluctuations
Tropospheric phase fluctuations
Refractive index of air n 6= 1:
n − 1 ≈10−6[
αPd
T+ β
Pw
T+ γ
Pw
T 2
]
Pw : Partial pressure of the water vapourT : Temperature of the water vapourFurthermore, the refractive index is a function of frequency (i.e.,the atmosphere is dispersive), especially at sub-mm frequenciesand close to the edges of the bandsHorizontal and line of sight variation in atmospheric propertieslead to phase errors and phase fluctuations
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First results at ALMA “Dry” fluctuations
Evidence for ‘dry’ fluctuationsWeak correlation between path and TB fluctuations
25 m baseline 90 m baseline
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Very dry conditions (0.4 mm PWV)
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First results at ALMA “Dry” fluctuations
Evidence for ‘dry’ fluctuationsBaseline length ∼ 25 m
8.0 8.2 8.4 8.6 8.8 9.0 9.2�0.06
�0.04
�0.02
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First results at ALMA “Dry” fluctuations
Evidence for ‘dry’ fluctuationsBaseline length ∼ 100 m
8.0 8.2 8.4 8.6 8.8 9.0 9.2�0.10
�0.05
0.00
0.05
0.10
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Summary
Outline
1 Introduction
2 Phase correction software
3 First results at ALMAGood examplesPoor examplesEffect on the beam“Dry” fluctuations
4 Summary
(University of Cambridge) WVR phase correction at ALMA March 2011 50 / 52
Summary
Summary
Working ALMA interferometer, working WVRs!Initial, but fully working, WVR phase correction software – usednow to do phase correction at ALMAPhase correction performance meets (or is very close) to thespecificationsImportant to obtain more test observations, especially:
On long baselinesMore consistent, long-term, measurements
Our work now focusing on:Improving the existing algorithmsUsing more information (e.g., weather stations, ancillaryinstruments)Implementing new algorithms (e.g., the ‘empirical’)Rolling out to users for testing and feed back
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Summary
Challenges
Dealing with widely different conditions at different antennas∼15 km baselines∼300 m altitude differenceridges, other topographical features
Dealing with cloudy conditionsNon-linear effects in a single observation
Probably non-negligible on long baselines
Using WVR signals for amplitude calibrationPhase transfer between different sources
What is maximum angular distance/airmass change for transfer?Improve astrometric accuracy?
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