VLA Self-calibration Tutorial-CASA5.7.0

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This CASA guide describes the basics of the self-calibration process and in particular how to choose parameters to achieve the best result. Even after the initial calibration of the dataset using the amplitude calibrator and the phase calibrator, there are likely to be residual phase and/or amplitude errors in the data. Self-calibration is the process of using an existing model, usually constructed from imaging the data itself, to reduce the remaining phase and amplitude errors in your image.

The dataset that will be used for this CASA tutorial is an observation of a massive galaxy cluster at z~1 which was taken with the goal to determine the morphology of the radio sources within the cluster.

Data for this Tutorial

Obtaining the Data

The original observation has been calibrated using the VLA CASA Pipeline. It can be downloaded from the archive by searching for the following SDM name: 17B-197.sb34290063.eb34589992.58039.86119096065.

We will instruct the archive to apply the calibration solutions derived by the original pipeline execution. Therefore, the measurement set (MS) we will be downloading will contain both the raw (uncalibrated) visibilities and the calibrated visibilities, which will appear in the 'DATA' and 'CORRECTED_DATA' columns of the MS, respectively.

The raw data alone is 11.4 GB, and this will grow to 20.5 GB after applying the calibration.

As an alternative to requesting these data from the archive you may instead download the calibrated MS directly here:

-- link to data download --

A smaller data set is also available (see section Splitting the Target Visibilities)

Observation Details

First, we will start CASA in the directory containing the data and then collect some basic information about the observation. The task listobs can be used to display the individual scans comprising the observation, the frequency setup, source list, and antenna locations.

# in CASA

A portion of the listobs output appears below:

           MeasurementSet Name:  17B-197.sb34290063.eb34589992.58039.86119096065.ms      MS Version 2
   Observer: Prof. Anthony H. Gonzalez     Project: uid://evla/pdb/34052589  
Observation: EVLA
Data records: 5290272       Total elapsed time = 2853 seconds
   Observed from   13-Oct-2017/20:40:09.0   to   13-Oct-2017/21:27:42.0 (UTC)

   ObservationID = 0         ArrayID = 0
  Date        Timerange (UTC)          Scan  FldId FieldName             nRows     SpwIds   Average Interval(s)    ScanIntent
  13-Oct-2017/20:40:09.0 - 20:45:03.0     1      0 J1549+5038              550368  [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]  [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3] [SYSTEM_CONFIGURATION#UNSPECIFIED]
              20:45:06.0 - 20:50:03.0     2      0 J1549+5038              555984  [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]  [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3] [CALIBRATE_AMPLI#UNSPECIFIED,CALIBRATE_PHASE#UNSPECIFIED]
              20:50:06.0 - 20:59:27.0     3      1 MOO_1506+5136          1050192  [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]  [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3] [OBSERVE_TARGET#UNSPECIFIED]
              20:59:30.0 - 21:00:51.0     4      0 J1549+5038              151632  [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]  [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3] [CALIBRATE_AMPLI#UNSPECIFIED,CALIBRATE_PHASE#UNSPECIFIED]
              21:00:54.0 - 21:10:15.0     5      1 MOO_1506+5136          1050192  [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]  [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3] [OBSERVE_TARGET#UNSPECIFIED]
              21:10:18.0 - 21:11:39.0     6      0 J1549+5038              151632  [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]  [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3] [CALIBRATE_AMPLI#UNSPECIFIED,CALIBRATE_PHASE#UNSPECIFIED]
              21:11:42.0 - 21:21:03.0     7      1 MOO_1506+5136          1050192  [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]  [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3] [OBSERVE_TARGET#UNSPECIFIED]
              21:21:06.0 - 21:22:27.0     8      0 J1549+5038              151632  [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]  [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3] [CALIBRATE_AMPLI#UNSPECIFIED,CALIBRATE_PHASE#UNSPECIFIED]
              21:22:30.0 - 21:27:03.0     9      2 3C286                   511056  [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]  [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3] [CALIBRATE_BANDPASS#UNSPECIFIED,CALIBRATE_FLUX#UNSPECIFIED]
              21:27:06.0 - 21:27:42.0    10      2 3C286                    67392  [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]  [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3] [CALIBRATE_BANDPASS#UNSPECIFIED,CALIBRATE_FLUX#UNSPECIFIED]
           (nRows = Total number of rows per scan) 
Fields: 3
  ID   Code Name                RA               Decl           Epoch   SrcId      nRows
  0    NONE J1549+5038          15:49:17.468534 + J2000   0        1561248
  1    NONE MOO_1506+5136       15:06:20.353700 + J2000   1        3150576
  2    NONE 3C286               13:31:08.287984 + J2000   2         578448
Spectral Windows:  (16 unique spectral windows and 1 unique polarization setups)
  SpwID  Name           #Chans   Frame   Ch0(MHz)  ChanWid(kHz)  TotBW(kHz) CtrFreq(MHz) BBC Num  Corrs          
  0      EVLA_C#A0C0#0      64   TOPO    4488.000      2000.000    128000.0   4551.0000       12  RR  RL  LR  LL
  1      EVLA_C#A0C0#1      64   TOPO    4616.000      2000.000    128000.0   4679.0000       12  RR  RL  LR  LL
  2      EVLA_C#A0C0#2      64   TOPO    4744.000      2000.000    128000.0   4807.0000       12  RR  RL  LR  LL
  3      EVLA_C#A0C0#3      64   TOPO    4872.000      2000.000    128000.0   4935.0000       12  RR  RL  LR  LL
  4      EVLA_C#A0C0#4      64   TOPO    5000.000      2000.000    128000.0   5063.0000       12  RR  RL  LR  LL
  5      EVLA_C#A0C0#5      64   TOPO    5128.000      2000.000    128000.0   5191.0000       12  RR  RL  LR  LL
  6      EVLA_C#A0C0#6      64   TOPO    5256.000      2000.000    128000.0   5319.0000       12  RR  RL  LR  LL
  7      EVLA_C#A0C0#7      64   TOPO    5384.000      2000.000    128000.0   5447.0000       12  RR  RL  LR  LL
  8      EVLA_C#B0D0#8      64   TOPO    5488.000      2000.000    128000.0   5551.0000       15  RR  RL  LR  LL
  9      EVLA_C#B0D0#9      64   TOPO    5616.000      2000.000    128000.0   5679.0000       15  RR  RL  LR  LL
  10     EVLA_C#B0D0#10     64   TOPO    5744.000      2000.000    128000.0   5807.0000       15  RR  RL  LR  LL
  11     EVLA_C#B0D0#11     64   TOPO    5872.000      2000.000    128000.0   5935.0000       15  RR  RL  LR  LL
  12     EVLA_C#B0D0#12     64   TOPO    6000.000      2000.000    128000.0   6063.0000       15  RR  RL  LR  LL
  13     EVLA_C#B0D0#13     64   TOPO    6128.000      2000.000    128000.0   6191.0000       15  RR  RL  LR  LL
  14     EVLA_C#B0D0#14     64   TOPO    6256.000      2000.000    128000.0   6319.0000       15  RR  RL  LR  LL
  15     EVLA_C#B0D0#15     64   TOPO    6384.000      2000.000    128000.0   6447.0000       15  RR  RL  LR  LL

Initial Inspection

Possible section here where we plot the phase vs. time for a calibrator scan. This will show at high signal-to-noise the magnitude and timescale of the phase fluctuations we will be trying to correct for with selfcal.

Splitting the Target Visibilities

It is essential that we split the calibrated visibilities for the target we want to self-calibrate, meaning that the visibilities of the target source get copied from the CORRECTED_DATA column of the pipeline calibrated MS to the DATA column of a new measurement set. CASA calibration tasks always operate by comparing the visibilities in the DATA column to the source model, where the source model is given by either the MODEL_DATA column, a model image or component list, or the default model of a 1 Jy point source at the phase center. In the same way that the calibration pipeline used the raw visibilities in the DATA column to solve for calibration tables and then created the CORRECTED_DATA column by dividing the DATA column by these tables, self-calibration will work by comparing the pipeline calibrated visibilities (in the DATA column of the split MS) to a model, solving for self-calibration tables, and then creating a new CORRECTED_DATA column by applying the self-calibration tables.

# in CASA
split(vis='17B-197.sb34290063.eb34589992.58039.86119096065.ms',datacolumn='corrected',field='1', correlation='RR,LL', outputvis='obj.ms')
  • vis='MOO_1506+5136_Cband.ms' : The input visibilities for split. Here, these are the visibilities produced by the pipeline.
  • datacolumn='corrected' : To copy the calibrated visibilities from the input MS.
  • field='1' : The field ID of the target we want to self-calibrate.
  • correlation='RR,LL' : To select only the parallel hand correlations. This will make the output data set smaller by about a factor of two.
  • outputvis='obj.ms' : The name of the new measurement set that split will create.

-- The output MS can be can be directly downloaded here: obj.ms (3.4 GB) --

Initial Imaging

Prior to solving for self-calibration solutions we need to make an initial image of the target field. This image will provide the starting model that is required by the calibration routines, and it will showcase why we need self-calibration for these data.

We will start with a large dirty image of the entire primary beam in order to get a feel for the sources in the clusters and any outliers that will need to be cleaned.

# in CASA
  • cell='0.24arcsec': The observations were taken in C-band in the B-configuration, which gives a resolution of 1.2". We want at least 5 image pixels across the resolution of 1.2", therefore we require a cell size of 0.24"/pixel.
  • imsize=2560: At C-band, the VLA primary beam is ~8'. In order to image the entire PB, we use an imsize of 2000 pixels.
  • niter=0: To make a dirty image we do not do any clean iterations.
  • interactive=False: Turn off the interactive feature to make the dirty image.
Figure 1A: The 8' dirty image using the Rainbow 3 color map.
Figure 1B: Zooming on the obejcts of interest in the 8' dirty image.

Now, we can make a preliminary clean image before rounds of self-calibration to use as a reference.

# in CASA
  • cell='0.24arcsec': The observations were taken in C-band in configuration B which gives a resolution of 1.2". Thus to 5 elements across the resolution of 1.2", we require a cell size of 0.24"/pixel.
  • imsize=750: For the science, we are only interested in the sources within ~1.5' of the cluster center. From the dirty image we also know that there are not any particularly problematic outliers in the field nearby, thus we create a 3' image. Thus to get an image of the entire PB, we use an imsize of 750 pixels.
  • deconvolver='mtmfs': Josh:I believe you recommended this to be based on the width of the band of observations with respect to the observing frequency -- I don't remember the qualitative guidelines for center_frequency/total_bandwidth. This corrects for the spectral slope when wideband width -- in this case 1.87GHz.
  • niter=1000: Set a relatively large number of iterations as a starting point.
  • interactive=True: So we can interactively place the mask.

For this preliminary clean, we place circles around each of the strong sources in turn:

  • The rightmost source (Figure 2A) -- continue forward and let it clean by pressing the green circle arrow
  • The leftmost double-lobed source (Figure 2B)
  • The middle source (Figure 2C)

At this point, there is no more emission that is believably real and we stop cleaning (a total of about 225 iterations)(see Figure 2D for an example of the artifacts that are not believable). The final cleaned image without the help of self-cal (Figure 3) shows a lot of artifacts and demonstrates a need for self-calibration (Josh: more details about why need self-cal? or why should we think self-cal would help?).

Figure 2A: The mask for rightmost source.
Figure 2B: The mask for the left double-lobed source.
File:Prelim clean 3.png
Figure 2C: The mask for the middle.
Figure 2D: The rightmost source after 3 iterations of adding masks. The emission around the current mask is characteristic of artifacts.
Figure 3: The preliminarily cleaned image.

Self-Calibration Process Outline

What is self-calibration (self-cal)? Self-calibration is the process of using an existing model, often constructed from imaging the data itself, to reduce the remaining phase and amplitude errors in your image. First you create a model using the data itself (using tclean), then iteratively improve this model with further rounds of self-cal (using Template:Gaincall to compare the model to the data and applycal to create a corrected data column).

A few typical cases in which self-cal will help improve the image (Josh: any other ideas?):

  • Extensive sidelobes/artifacts from the source of interest or outliers

Each "round" of self-calibration follows a general procedure:

  1. Conservatively clean target and save the model. Using interactive clean, only include the pixels that you are absolutely certain are real emission as this will be the basis of the model for calibration.
  2. Use gaincal with a particular solution interval to calculate a table of solutions.
  3. Check the solutions using plotcal.
  4. Determine if the solutions should be applied (or if it is time to stop). Is there structure to the solutions?
  5. Use applycal to apply the table of solutions to the data.
  6. Use split to make the calibrated data with the applied solutions, the new data (the starting point for the next round of self-cal).
  7. Start the next round of self-cal.

This guide will cover:

  • How to do the self-calibration process in general.
  • How to determine optimal selfcal parameters (e.g. solution interval).
  • When to stop self-cal.

First Round of Self-Calibration

For this first round of self-cal, we will explore various parameters in order to settle on the optimal parameters. First, we make a conservative clean, meaning that we include in the mask only the pixels that we are absolutely convinced that is real emission.

Preliminary Clean

# in CASA

We keep the same parameters as before, but reduce the number of iterations and include savemodel='modelcolumn' which is crucial. By including savemodel, we ensure that the model is explicitly saved to the model column.

Show an example of the masks we used and say approximately how many iterations. It looks like we did just the left source and the middle source. Do you perhaps know why we didn't include the right source at all in the mask? This is perplexing to me as that is the main source that we would want to model I would think.

Check that the model saved?

Choose a Solution Interval

Next, we investigate which solution interval (solint) is optimal for this particular dataset. We will do this by generating and viewing the corrections calculated over several solution intervals (namely on the order of the integration time, 15 seconds, and 45 seconds) using gaincal.

# in CASA
  • caltable='sc1_tb_int':'Name the calibration tables something intuitive to distinguish each one. In this case, sc1 for self-calibration round 1, tb for table, and int for a solution interval of the integration time.
  • solint='int': For this first calibration table, we choose the solutions to be calculated on the level of the integration time (3 seconds in this case) in order to get a sense of the structure and durations of the variations.
  • refant='ea24': Choose a reference antenna that is near the center of the array but not the exact center (Josh: I think that is why we chose this one?). In order to view what antennas are near the center use plotants.
  • calmode='p': Start with phase only calibration as that is where the most gain is to be had (there is a way more technical way to say that).
  • gaintype='T': It is often a good idea to combine the polarizations to understand the overall data.

To view these solutions, we use plotcal.

# in CASA
  • xaxis='time' & yaxis='phase': View the phase variations over time with respect to antenna 24.
  • iteration='antenna': View the corrections for each antenna.
  • subplot=321: It can be helpful to view multiple plots at once, as we will be scrolling through the solutions for each antenna. In this case, 3 rows and two columns.

Show examples of a few of these. gaincal calculates the gains for each antenna/spwid are determined from the ratio of the data column (raw data), divided by the model column, for the specified data selection.(Josh: a bit more about technically what we are looking at and how they affect the data? -- these are the variations in phase between pairs of antennas which can cause the artifacts?) Each color represents a different spectral window. There is definitely structure we want to correct for. We use this solint = int to get a sense of on what time scale the variations occur and the amplitude of the variations. Use the zoom in feature to zoom in on these variations and estimate what the time interval of these variations are. The solint of int shows the variations, but we postulate that a 15 second or 45 second interval would catch these overall variations better.

# in CASA

Images of both of these. Comparing the two, we see that they both encapsulate the structure of the variations, however solint=15s encapsulates the variations the best. Thus we choose to apply sc1_tb_15s.gain. Note the amplitude of these variations in sc1_tb_15s.gain; they are typically up to 40 degrees and -60 yo -80 degrees.

Apply Calibration

# in CASA

Other potential intervals that could be useful to use are 5 seconds and 60 seconds. Additionally, if the plotcal reveals no structure in these intervals, you can combine the spws together and apply a combined solution by setting combine='spw' (you will have to set spwmap=[0 x number of spectral windows] in apply cal in order to tell CASA to apply the combined solution to all of the spectral windows -- in this case spwmap = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0])

Our goal is to create a better model of the source using the data itself to create a better image. The process with calculating corrections and applying them has (if done correctly) reduced the errors allowing us to make a better image and create a better model. applycal applies the solutions to the (raw) MS DATA column and writes the calibrated data into the CORRECTED_DATA column, thus for determining the next order/level of corrections during the next round of calibration we need to make the corrected data column the data column.

# in CASA

Second Round of Self-Calibration

We will go through the same process again: image, gaincal, applycal, and image. This time the image we create at first will be an indication if the solutions we applied made a difference.

# in CASA

Larger mask.

# in CASA
  • solint='15s': Since we found a good interval of 15 seconds previously, we move forward with 15s.

Josh: do we want to suggest/show other solution intervals? Do we want to comment on using various intervals -- when is it useful to do several rounds with one interval versus several rounds with each a different solint?

View the solutions.

# in CASA

We can see our corrections made a difference. In particular notice the amplitude of the phase variations, instead of +_60-80 degress they are fractions of a degree. This indicates that we have corrected for a majority of the variations.

When to stop: (looking at amplitude of corrections, image has improved/dynamical range)

Notes about amplitude corrections (?) (mode='ap' or 'a').

When self-cal helped, but didn't improve the image enough: (suggestions for other things to do)