Atacama Large Millimeter/submillimeter Array Expanded Very Large Array Robert C. Byrd Green Bank Telescope Very Long Baseline Array

Transcription

1 Atacama Large Millimeter/submillimeter Array Expanded Very Large Array Robert C. Byrd Green Bank Telescope Very Long Baseline Array

2 Basics of Interferometry Data Reduction Scott Schnee (NRAO) ALMA Data workshop Dec. 1, 2011 Atacama Large Millimeter/submillimeter Array Expanded Very Large Array Robert C. Byrd Green Bank Telescope Very Long Baseline Array

3 Outline The general idea First look at your data Basic data reduction in CASA Flagging 3

4 The General Idea: Fourier Transforms 1. An interferometer measures the interference pattern produced by two apertures. 2. The interference pattern is directly related to the source brightness. In particular, for small fields of view the complex visibility, V(u,v), is the 2D Fourier transform of the brightness on the sky, T(x,y) 4

5 The General Idea: Fourier Transforms y image plane x T(x,y) Fourier space/domain Image space/domain uv plane 5

6 The General Idea: Amplitudes and Phases V = I max I min I max + I min = Fringe Amplitude Average Intensity b 1 Δθ=λ/b The visibility is a complex quantity: - amplitude tells how much of a certain frequency component - phase tells where this component is located phase 6

7 The General Idea: Amplitudes and Phases Each pair of antennas will generate a visibility (amplitude and phase) Every integration: time interval Every channel: frequency interval Goal of calibration is to correct these amplitudes and phases for atmospheric and instrumental effects 7

8 The General Idea: Corrupted Data Variations in the amount of precipitable water vapor (PWV) cause phase fluctuations and result in Low coherence (loss of sensitivity) Radio seeing, typically 1ʺ at 1 mm Anomalous pointing offsets Anomalous delay offsets Patches of air with different water vapor content (and hence index of refraction) affect the incoming wave front differently. 8

9 The General Idea: Corrupted Data The atmosphere can absorb/emit significantly at (sub)millimeter wavelengths, creating phase and amplitude variations that need to be removed from measurement sets The antennas and other parts of the array also introduce noise into data sets 9

10 The General Idea: Calibration Basic calibration involves observing calibrators of known brightness and morphology Quasars (bright point sources) Solar system objects (well-characterized, so easily modeled) Determine corrections that make the observations fit the model Derive the changes to amplitude and phase (complex gain) vs frequency and time Apply the corrections from the calibration data to the science target data Interpolating the derived calibration solutions 10

11 First Look at Your Data In CASA: listobs(vis= my_data.ms ) Crystal Brogan s talk will describe results in detail Identify calibrators and science targets from intents Absolute flux calibrator to scale amplitudes Solar system object or quasar Bandpass calibrator to solve for variations with frequency Bright quasar Gain calibrator to solve for variations with time Reasonably bright quasars near science targets Every data set is different in terms of number of calibrators and science targets and uniqueness of calibrators 11

12 Basic Data Reduction: Bandpass Calibration Determine the variations of phase and amplitude with frequency Assume amplitude/phase vs. frequency and find solution to make data match the model Default assumption: 1 Jy, no spectral index Put in custom spectral index using setjy Calibrator information being built up now, accessible via OT Often assumed to be independent of time Less important that bandpass calibrator be near science target 12

13 Basic Data Reduction: Bandpass Calibration In CASA: Use the bandpass task Determine channel-to-channel variation Determine smooth function to remove variation Use applycal to apply the bandpass solution to other sources 13

14 Basic Data Reduction: Bandpass Calibration Baselines to one antenna Phase Before Bandpass Calibration 14

15 Basic Data Reduction: Bandpass Calibration Antenna-based Bandpass Solution: Phase 15

16 Basic Data Reduction: Bandpass Calibration Baselines to one antenna Amplitude Before Bandpass Calibration 16

17 Basic Data Reduction: Bandpass Calibration Antenna-based Bandpass Solution: Amplitude 17

18 Basic Data Reduction: Gain Calibration Determine the variations of phase and amplitude with time Assume amp/phase vs. time and find solution to make data match the model Often assumed to be independent of frequency Important that gain calibrator be near science target In CASA: Use the gaincal task Determine integration-to-integration variation Determine smooth function to remove variation Use applycal to apply the gain solution to other sources 18

19 Basic Data Reduction: Gain Calibration Phase Raw data One baseline Two calibrators colorized by source From TW Hydra Band 7 ALMA CASA Guide Time 19

20 Basic Data Reduction: Gain Calibration Phase Model data All baselines Two calibrators colorized by source From TW Hydra Band 7 ALMA CASA Guide Time 20

21 Basic Data Reduction: Gain Calibration Phase Corrected data All baselines Two calibrators colorized by source From TW Hydra Band 7 ALMA CASA Guide Time 21

22 Basic Data Reduction: Gain Calibration Amplitude Raw data All baselines Two calibrators colorized by source From TW Hydra Band 7 ALMA CASA Guide Time 22

23 Basic Data Reduction: Gain Calibration Amplitude Model data All baselines Two calibrators colorized by source From TW Hydra Band 7 ALMA CASA Guide Time 23

24 Basic Data Reduction: Gain Calibration Amplitude Corrected data All baselines Two calibrators colorized by source From TW Hydra Band 7 ALMA CASA Guide Time 24

25 Basic Data Reduction: Absolute Flux Calibration In CASA: setjy(vis= my_data.ms, field= Titan ) Use model for Titan in CASA to create a model column containing the expected observations Done very early in the calibration process In CASA: fluxscale is the task that uses the observations and model of the absolute flux calibrator (e.g. Titan) to derive the fluxes of the bandpass and gain calibrators Done at the end of the calibration process, after gaincal The derived amplitude vs. time corrections for the gain calibrator are then applied to the science target setjy, fluxscale, and gaincal will be discussed in Amy Kimball s talk and are presented in the CASA Guides 25

26 Basic Data Reduction: Absolute Flux Calibration Raw Data Amplitude vs. uv-distance Model Data CARMA observations of MARS from M99 CASA Guide 26

27 Shadowing Issue at low elevations Issue for compact arrays Flagging: Initial Flagging In CASA: flagdata(vis= my_data.ms, mode= shadow ) Observing Log Many observatories will note weather or hardware problems that affect the data. Other obvious errors 27

28 Flagging: Initial Flagging Tsys plots NGC 3256 ALMA CASA Guide 28

29 Flagging: What to Look For Plots of amplitude and phase vs. time and frequency Iterate over Antenna Spectral window Source Make plots of calibrators first Easier to find problems in observations of bright point source Harder to find problems in observations of a faint and extended source 29

30 Flagging: What to Look For Smoothly varying phases and amplitudes can be calibrated Discontinuities can not be calibrated Features in the calibrators that may not be in the target data can cause problems 30

31 Flagging: What to Look For From TW Hydra ALMA Guide Color: Polarization One spectral window (spw) plotted 31

32 Flagging: What to Look For Amplitude vs. Frequency - Birdies From TW Hydra CASA Guide Brown and Green show phase calibrators Orange shows TW Hya 32

33 Flagging: What to Look For Edge Channels Amplitude vs. Channel Amplitude vs. Channel 33

34 Flagging: What to Look For Data that should be flagged Amplitude vs. Channel 34

35 Flagging: What to Look For From TW Hydra Band 7 Guide Spectral line in Titan (Bandpass Calibrator) 35

36 Flagging: What to Look For Phase vs. Time on Gain Calibrator From Antennae ALMA CASA Guide First batch of data Second batch of data 36

37 Data Review: plotms Top Tabs Side Tabs Control Panel Graphics Panel Tools Panel ALMA Data Reduction Workshop 1 Dec

38 Data Review: plotms Control panel: Data The modification of certain parameters may not be applied if Plot is clicked and force reload is unchecked. ALMA Data Reduction Workshop 1 Dec

39 Data Review: plotms Control panel: Axes Drop down menus to select x and y axes: time, channel, frequency, velocity, amplitude, phase, uvdist, elevation, etc. ALMA Data Reduction Workshop 1 Dec

40 Data Review: plotms Iteration Scan Field Spw Baseline Antenna Tool panel ALMA Data Reduction Workshop 1 Dec

41 Data Review: plotms Transformations Frame: TOPO, GEO, BARY, LSRK, LSRD, etc.. ALMA Data Reduction Workshop 1 Dec

42 Data Review: plotms Display Colorize by: Scan Field Spw Antenna1 Antenna2 Baseline Channel Correlation ALMA Data Reduction Workshop 1 Dec

43 Flagging: Locating Bad Data - plotms Draw a box around the suspected bad data. 43

44 Flagging: Locating Bad Data - plotms Click locate and CASA will send information about the data to the logger. 44

45 Flagging: Locating Bad Data - plotms Bad data can be flagged by pressing this button or using the flagdata task at the CASA prompt. 45

46 Flagging: Locating Bad Data - plotms Flagger s remorse can be corrected by unflagging good data 46

47 Sage Advice From Rick Perley to a much younger Scott Schnee: When in doubt, throw it out. 47

48 Possible Flagging and Calibration Recipe EXAMINE bandpass/flux calibrator(s) FLAG bandpass/flux calibrators Iterate APPLY bandpass/flux calibra?on to itself APPLY bandpass/flux cal to gain cal sources EXAMINE gain calibra?on sources FLAG gain calibra?on sources Iterate APPLY gain calibra?on to itself APPLY bandpass/flux/gain cal to targets EXAMINE targets Repeat as necessary FLAG targets 48

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50 NAASC 50