VLA CASA Bandpass Slope-CASA4.5.2

This CASA Guide is for CASA version 4.5.2

Overview

For the standard VLA flux density calibrators 3C138, 3C147, 3C286, and 3C48, CASA includes a spatial and spectral model that is applied for bandpass calibration. This model removed the source characteristics, resulting in a calibration solution that is a representation of the instrumental and atmospheric corrections. These VLA standard calibrators, however, exhibit a negative spectral index and are relatively weak at high frequencies.

Although the standard VLA flux density calibrators are usually still bright enough for absolute flux density calibration, a good bandpass determination—which is very important for spectral line observations—requires large signal-to-noise ratios derived from either a long integration time or a very strong source (see the Spectral Line Guide for Observing). Observations of non-standard, but strong, bandpass calibrators are therefore common at high frequencies. Unfortunately, such sources are likely variable and no a priori flux density model is available. In particular, these sources exhibit an unknown and maybe variable spectral slope, which, if not accounted for, will create an error in the bandpass calibration. This tutorial describes how to model a spectral slope and how to correct the bandpass solution for this effect.

Data used in this guide are taken in wide 3-bit mode for the protostar G192.16-3.84 in Ka-band with basebands centered at 29 and 36.5 GHz. Each baseband has over 4 GHz of bandwidth comprising thirty-two 128-MHz spectral windows.

If you are new to CASA, or with VLA data reduction in CASA, it is strongly recommended that you start with either the EVLA Continuum Tutorial 3C391 or Getting Started in CASA before proceeding with this tutorial.

Obtaining the Data

As this tutorial concerns bandpass calibration, all sources other than the flux density and bandpass calibrator scans were removed from the MS. All pre-calibration steps including flagging, antenna position offsets, requantizer gains, opacity corrections, and gain-elevation curves were applied. The original data (TVER0004.sb14459364.eb14492359.56295.26287841435) can be obtained through the NRAO archive and has a raw size of 57.04 GB.

The trimmed measurement set can be downloaded directly from http://casa.nrao.edu/Data/EVLA/G192/G192-BP.ms.tar.gz (dataset size: 3.4 GB)

Your first step will be to unzip and untar the file in a terminal (before you start CASA):

tar -xzvf G192-BP.ms.tar.gz

Starting CASA

To start CASA, type:

casa

This will run a script initializing CASA and setting paths appropriately. The script will also create two files called ipython-<unique-stamp>.log (which contains a record of all the text you enter at the CASA prompt) as well as casapy-<unique-stamp>.log (which will contain all the messages that are printed to the CASA logger window). It is recommended that you keep your log files intact&#151;you may need them to remind you of the last step you completed in your data reduction. (It is also a good idea to include your log files when submitting a help desk ticket).

Once CASA has started, a logger window will appear. Note that you can rescale this window or change the font size (under the View menu option) as desired.

Examining the Measurement Set (MS)

We use listobs to summarize our MS:

# In CASA: listobs on the initial data set
listobs('G192-BP.ms', listfile='G192_listobs.txt')

This will write the output to a file called G192_listobs.txt, which we can print to the terminal using various Unix/Linux commands such as cat, less, or more:

# In CASA
cat G192_listobs.txt

================================================================================
           MeasurementSet Name:  /lustre/aoc/sciops/jott/casa/topicalguide/bandpass/new/G192-BP.ms      MS Version 2
================================================================================
   Observer: Dr. Debra Shepherd     Project: uid://evla/pdb/7303457  
Observation: EVLA
Data records: 1769355       Total elapsed time = 4563 seconds
   Observed from   03-Jan-2013/06:31:48.0   to   03-Jan-2013/07:47:51.0 (UTC)

   ObservationID = 0         ArrayID = 0
  Date        Timerange (UTC)          Scan  FldId FieldName             nRows     SpwIds   Average Interval(s)    ScanIntent
  03-Jan-2013/06:31:48.0 - 06:36:42.0     6      0 3C147                   704865  [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63]  [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5.94, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6] [CALIBRATE_FLUX#UNSPECIFIED,OBSERVE_TARGET#UNSPECIFIED]
              07:40:27.0 - 07:47:51.0    64      1 3c84-J0319+413         1064490  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]  [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6] [CALIBRATE_BANDPASS#UNSPECIFIED,OBSERVE_TARGET#UNSPECIFIED]
           (nRows = Total number of rows per scan) 
Fields: 2
  ID   Code Name                RA               Decl           Epoch   SrcId      nRows
  0    E    3C147               05:42:36.137916 +49.51.07.23356 J2000   0         704865
  1    F    3c84-J0319+413      03:19:48.160102 +41.30.42.10305 J2000   1        1064490
Spectral Windows:  (64 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_KA#A1C1#2     128   TOPO   34476.000      1000.000    128000.0  34539.5000       10  RR  LL
  1      EVLA_KA#A1C1#3     128   TOPO   34604.000      1000.000    128000.0  34667.5000       10  RR  LL
  2      EVLA_KA#A1C1#4     128   TOPO   34732.000      1000.000    128000.0  34795.5000       10  RR  LL
  3      EVLA_KA#A1C1#5     128   TOPO   34860.000      1000.000    128000.0  34923.5000       10  RR  LL

<snip>

  13     EVLA_KA#A1C1#15    128   TOPO   36140.000      1000.000    128000.0  36203.5000       10  RR  LL
  14     EVLA_KA#A1C1#16    128   TOPO   36268.000      1000.000    128000.0  36331.5000       10  RR  LL
  15     EVLA_KA#A1C1#17    128   TOPO   36396.000      1000.000    128000.0  36459.5000       10  RR  LL
  16     EVLA_KA#A2C2#18    128   TOPO   36476.000      1000.000    128000.0  36539.5000       11  RR  LL
  17     EVLA_KA#A2C2#19    128   TOPO   36604.000      1000.000    128000.0  36667.5000       11  RR  LL
  18     EVLA_KA#A2C2#20    128   TOPO   36732.000      1000.000    128000.0  36795.5000       11  RR  LL
 
<snip>

  29     EVLA_KA#A2C2#31    128   TOPO   38140.000      1000.000    128000.0  38203.5000       11  RR  LL
  30     EVLA_KA#A2C2#32    128   TOPO   38268.000      1000.000    128000.0  38331.5000       11  RR  LL
  31     EVLA_KA#A2C2#33    128   TOPO   38396.000      1000.000    128000.0  38459.5000       11  RR  LL
  32     EVLA_KA#B1D1#34    128   TOPO   26976.000      1000.000    128000.0  27039.5000       13  RR  LL
  33     EVLA_KA#B1D1#35    128   TOPO   27104.000      1000.000    128000.0  27167.5000       13  RR  LL
  34     EVLA_KA#B1D1#36    128   TOPO   27232.000      1000.000    128000.0  27295.5000       13  RR  LL

<snip>

  45     EVLA_KA#B1D1#47    128   TOPO   28640.000      1000.000    128000.0  28703.5000       13  RR  LL
  46     EVLA_KA#B1D1#48    128   TOPO   28768.000      1000.000    128000.0  28831.5000       13  RR  LL
  47     EVLA_KA#B1D1#49    128   TOPO   28896.000      1000.000    128000.0  28959.5000       13  RR  LL
  48     EVLA_KA#B2D2#50    128   TOPO   28976.000      1000.000    128000.0  29039.5000       14  RR  LL
  49     EVLA_KA#B2D2#51    128   TOPO   29104.000      1000.000    128000.0  29167.5000       14  RR  LL
  50     EVLA_KA#B2D2#52    128   TOPO   29232.000      1000.000    128000.0  29295.5000       14  RR  LL

<snip>

  61     EVLA_KA#B2D2#63    128   TOPO   30640.000      1000.000    128000.0  30703.5000       14  RR  LL
  62     EVLA_KA#B2D2#64    128   TOPO   30768.000      1000.000    128000.0  30831.5000       14  RR  LL
  63     EVLA_KA#B2D2#65    128   TOPO   30896.000      1000.000    128000.0  30959.5000       14  RR  LL
Sources: 128
  ID   Name                SpwId RestFreq(MHz)  SysVel(km/s) 
  0    3C147               0     -              -            
  0    3C147               1     -              -            
  0    3C147               2     -              -            

<snip>

  0    3C147               61    -              -            
  0    3C147               62    -              -            
  0    3C147               63    -              -            
  1    3c84-J0319+413      0     -              -            
  1    3c84-J0319+413      1     -              -            
  1    3c84-J0319+413      2     -              -            

<snip>

  1    3c84-J0319+413      61    -              -            
  1    3c84-J0319+413      62    -              -            
  1    3c84-J0319+413      63    -              -            

<snip>

We have trimmed the MS to contain only scans on the flux calibrator 3C147 (field 0) and the bandpass calibrator 3C84 (field 1) but retained all 64 spectral windows, each 128 MHz wide and containing one hundred twenty-eight 1 MHz channels.

Setting the Model of the Flux Density Calibrator

To start, we insert the spectral (using the 'Perley-Butler 2013' standard) and spatial (3C147_A.im for Ka-band) models for the flux density calibrator 3C147 (field 0) with the setjy task:

# In CASA: model for the flux density calibrator
setjy(vis='G192-BP.ms', field='0', scalebychan=True, \
      standard='Perley-Butler 2013', model='3C147_A.im')

Figure 1: plotms of model amp vs freq for 3C147

• scalebychan=True: If scalebychan=False setjy would use a single value per spectral window.

Inspecting the logger report shows that 3C147 has a flux density ranging from ~1.0-1.47 Jy across all spws.

We can plot the model data using plotms (Figure 1):

# In CASA
plotms(vis='G192-BP.ms', field='0', antenna='ea03', \
       xaxis='freq', yaxis='amp', ydatacolumn='model',coloraxis='ant2')

This plot shows baselines to antenna ea03. Since we provided both a spectral and a spatial model for this well resolved calibrator, each baseline has a somewhat different behavior.

Calibrating delays and initial bandpass solutions

As a first step, we need to specify a reference antenna for all phase calibrations. It is desirable to use an antenna that is near the center of the array and that has a minimum of flags. The array can be mapped with plotants:

# In CASA: plotting antenna locations
plotants(vis='G192-BP.ms')

Although the plot is a bit crowded (Figure 2), a zoom in (the icon with the magnifying glass) shows that ea05 sits close to the center and appears to be a good choice.

Figure 2: plotants plotter

Figure 3a: plotcal G0 phase ant 0~15

Figure 3b: plotcal G0 phase ant 16~26

Figure 4: plotcal K0 delay vs. antenna

We start with a phase-only calibration solution on a narrow range of channels near the center of each spectral window on the bandpass calibrator 3C84 to flatten them with respect to time before solving for the bandpass. This will remove most of the decorrelation of the signal. The range 60~68 should work, and we derive a solution for each individual integration:

# In CASA: phase only calibration
gaincal(vis='G192-BP.ms', caltable='calG192.G0', \
        field='1', spw='*:60~68', \
        gaintype='G', refant='ea05', calmode='p', \
        solint='int', minsnr=3)

• refant='ea05' : Use ea05 as the reference antenna
• solint='int' : Do a per-integration solve (every 6 seconds, since we've time-averaged the data).
• minsnr=3 : Apply a minimum signal-to-noise cutoff. Solutions with less than this value will be flagged.
• gaintable is not set here as we have already applied pre-calibrations.

Plot the phase solutions (using full phase range, -180 to 180, instead of autorange):

# In CASA
plotcal(caltable='calG192.G0', xaxis='time', yaxis='phase', \
        iteration='antenna', plotrange=[-1,-1,-180,180])

Plot the phase solutions. We will produce multipanel plots and write to output files (Figures 3a & 3b). The output files generated are PNG files and can be viewed within CASA by executing an external viewer program, e.g., !xv plotG192_plotcal_G0p1.png; or by running any number of image viewing applications such as xv, Preview, Gimp, Photoshop, etc., external to CASA at the OS level. (Note that the hardcopy only shows the first page):

# In CASA
plotcal(caltable='calG192.G0', xaxis='time', yaxis='phase', \
        antenna='0~10,12~15', subplot=531, iteration='antenna', \
        plotrange=[-1,-1,-180,180], fontsize=8.0, \
        markersize=3.0, figfile='plotG192_plotcal_G0p1.png')
plotcal(caltable='calG192.G0', xaxis='time', yaxis='phase', \
        antenna='16~26', subplot=531, iteration='antenna', \
        plotrange=[-1,-1,-180,180], fontsize=8.0, \
        markersize=3.0, figfile='plotG192_plotcal_G0p2.png')

We can now solve for the residual delays using the gaintype='K' option in gaincal. Note that this currently does not do a global fringe-fitting solution for delays, but instead does a baseline-based delay solution per spw for all baselines to the reference antenna, treating these as antenna-based delays. In most cases, with high enough S/N to get baseline-based delay solutions, this will suffice. We avoid the edge channels of each spectral window by selecting channels 5~122:

# In CASA: residual delays
gaincal(vis='G192-BP.ms', caltable='calG192.K0', \
        field='1', spw='*:5~122', gaintype='K', \
        gaintable=['calG192.G0'],
        refant='ea05', solint='inf', minsnr=3)

Note that we have also pre-applied our initial phase table calG192.G0.

Alternatively, you can also derive a delay across all spws of a baseband. If this is desired, use combine='spw' in gaincal and run the task for each baseband separately. The solutions from the second and following runs can be appended to the same calibration table via append=T.

Now plot the delays, in nanoseconds, as a function of antenna index (you will get one for each spw and polarization):

# In CASA
plotcal(caltable='calG192.K0', xaxis='antenna', yaxis='delay')

The delays range from around -5 to 4 nanoseconds (Figure 4).

Now solve for the antenna bandpasses using the previously generated tables calG192.G0 and calG192.K0:

# In CASA: antenna bandpasses
bandpass(vis='G192-BP.ms', caltable='calG192.B0', \
         gaintable=['calG192.G0', 'calG192.K0'], \
         field='1', refant='ea05', solnorm=False, \
         bandtype='B', solint='inf')

WARNING: You must set solnorm=False here or later on you will find some offsets among spws due to the way the amplitude scaling adjusts weights internally during solving.

You will see, in the terminal window, some reports of solutions failing due to "Insufficient unflagged antennas"&#151;note that these are for bad channels that have been pre-flagged.

Plot the resulting bandpasses in amplitude and phase. Note that the first panel with ea01 is empty as it is completely flagged. Proceed to ea06 to see the plots as shown in Figures 5a, 5b, 6a, and 6b:

# In CASA
plotcal(caltable='calG192.B0', xaxis='freq', yaxis='amp', \
        spw='0~31', iteration='antenna')
#
plotcal(caltable='calG192.B0', xaxis='freq', yaxis='amp', \
        spw='32~63', iteration='antenna')
#
plotcal(caltable='calG192.B0', xaxis='freq', yaxis='phase', \
        iteration='antenna', spw='0~31', \
        plotrange=[-1,-1,-180,180])
#
plotcal(caltable='calG192.B0', xaxis='freq', yaxis='phase', \
        iteration='antenna', spw='32~63', \
        plotrange=[-1,-1,-180,180])

Figure 5a: plotcal B0 bandpass amp ant ea06 spw 0-31

Figure 5b: plotcal B0 bandpass amp ant ea06 spw 32-63

Figure 6a: plotcal B0 bandpass phase ant ea06 spw 0-31

Figure 6b: plotcal B0 bandpass phase ant ea06 spw 32-63

Bootstrapping the bandpass calibrator spectrum

Since there is no a priori spectral information for our chosen bandpass calibrator&#151;3C84&#151;we need to bootstrap to find its spectral index, then recalibrate with this information in order to avoid folding the intrinsic spectral shape of 3C84 into our calibration.

First, we again do a phase-only calibration solution, this time for both the bandpass and the flux density calibrator. This will correct for decorrelation of the signals. Again we use the channel range 60~68 and apply the bandpass and delay calibration tables:

# In CASA: flux and bandpass calibrators gain
gaincal(vis='G192-BP.ms', caltable='calG192.G1p', field='0,1', \
        gaintable=['calG192.K0', 'calG192.B0'], \
        gaintype='G', refant='ea05', calmode='p', solint='int', minsnr=3)

Now we are ready to solve for both, phase and gain for each scan:

# In CASA: flux and bandpass calibrators gain
gaincal(vis='G192-BP.ms', caltable='calG192.G1', field='0,1', \
        gaintable=['calG192.K0', 'calG192.B0','calG192.G1p'], \
        gaintype='G', refant='ea05', calmode='ap', solint='inf', minsnr=3)

With gain solutions for the flux density and bandpass calibrators, we can now use fluxscale to scale the gain amplitudes of the bandpass calibrator using those of the flux density calibrator:

# In CASA: bandpass calibrator gain amplitudes scaling
flux1 = fluxscale(vis='G192-BP.ms', caltable='calG192.G1', \
                  fluxtable='calG192.F1', reference='0', \
                  transfer='1', listfile='3C84.fluxinfo', fitorder=1)

• flux1 = fluxscale(...): we allow fluxscale to use the variable flux1 for the output Python dictionary that is returned, which has information about the flux scaling. You can inspect the output dictionary flux1 by typing "print flux1" at the CASA command line.
• fluxtable='calG192.F1': this is the output scaled gain table. Since we are only using this to find the spectral index of 3C84, we won't be using this table.
• listfile='3C84.fluxinfo': an output file that contains the derived flux values and fit information.
• fitorder=1: only find a spectral index, ignoring curvature in the spectrum.
• reference='0': the reference field from which the flux scaling is transferred (here: the flux calibrator 3C147, field 0)
• transfer='1': the target field to which the flux scaling is transferred (here: the bandpass calibrator 3C84, field 1)

The last line in the file (and displayed in the logger) shows:

Fitted spectrum for 3c84-J0319+413 with fitorder=1: Flux density = 29.0282 +/- 0.0308648 (freq=32.5128 GHz) spidx=-0.538758 +/- 0.00882913

Figure 7: 3C84 flux values returned by fluxscale

Figure 8: plotms of model amp vs freq for 3C84

Using the information in the returned flux1 dictionary, we can plot the derived spectrum (Figure 7):

# In CASA
freq = flux1['freq'] / 1e9
spw_list = range(0,64)
spw_str = []
for i in spw_list:
   thisspw = str(i)
   spw_str.append(thisspw)

# In CASA
bootstrapped_fluxes = []
for j in spw_str:
    thisflux = flux1['1'][j]['fluxd'][0]
    if thisflux ==None:
        continue
    else:
        bootstrapped_fluxes.append(thisflux)

# In CASA - this section creates the plot seen in Figure 7
pl.clf()
pl.plot(freq, bootstrapped_fluxes, 'bo')
pl.xlabel('Frequency (GHz)')
pl.ylabel('Flux Density (Jy)')
pl.title('3C84')
pl.show()

We can use the model from fluxscale to fill the MODEL column with 3C84's spectral information using setjy. With standard='fluxscale', we can directly use the flux1 Python dictionary as input via fluxdict:

# In CASA: spectral information
setjy(vis='G192-BP.ms', field='1', scalebychan=True, \
      standard = 'fluxscale', fluxdict=flux1)

Checking with plotms that the data have been appropriately filled (Figure 8):

# In CASA
plotms(vis='G192-BP.ms', field='1', antenna='ea05&ea02', \
       xaxis='freq', yaxis='amp', ydatacolumn='model')

We redo the previous calibration using this new model information. Although the commands are the same as what we issued earlier, keep in mind that the model values for the bandpass calibrator have changed, and therefore the results of these calibration calculations will differ:

# In CASA: phase only recalibration
gaincal(vis='G192-BP.ms', caltable='calG192.G0.b', \
        field='1', spw='*:60~68', \
        gaintype='G', refant='ea05', calmode='p', \
        solint='int', minsnr=3) 
# In CASA: residual delays recalibration
gaincal(vis='G192-BP.ms', caltable='calG192.K0.b', \
        gaintable=['calG192.G0.b'], \
        field='1', spw='*:5~122', gaintype='K', \
        refant='ea05', solint='inf', minsnr=3)
# In CASA: antenna bandpasses recalibration
bandpass(vis='G192-BP.ms', caltable='calG192.B0.b', \
         gaintable=['calG192.G0.b', 'calG192.K0.b'], \
         field='1', refant='ea05', solnorm=False, \
         bandtype='B', solint='inf')

Finally we inspect these solutions (Figures 9a, 9b, 10a, and 10b):

# In CASA - Figure 9a
plotcal(caltable='calG192.B0.b', xaxis='freq', yaxis='amp', \
        spw='0~31', iteration='antenna')
# Figure 9b
plotcal(caltable='calG192.B0.b', xaxis='freq', yaxis='amp', \
        spw='32~63', iteration='antenna')
# Figure 10a
plotcal(caltable='calG192.B0.b', xaxis='freq', yaxis='phase', \
        iteration='antenna', spw='0~31', \
        plotrange=[-1,-1,-180,180])
# Figure 10b
plotcal(caltable='calG192.B0.b', xaxis='freq', yaxis='phase', \
        iteration='antenna', spw='32~63', \
        plotrange=[-1,-1,-180,180])

Figure 9a: plotcal B0 bootstrapped bandpass amp ant ea06 spw 0-31

Figure 9b: plotcal B0 bootstrapped bandpass amp ant ea06 spw 32-63

Figure 10a: plotcal B0 bootstrapped bandpass phase ant ea06 spw 0-31

Figure 10b: plotcal B0 bootstrapped bandpass phase ant ea06 spw 32-63

They look virtually unchanged from the previous solutions, with the exception that the amplitude scaling is corrected for the spectrum of 3C84. Now that we have the final version of our delay and bandpass calibration tables, calG192.K0.b and calG192.B0.b can be used for all subsequent calibration steps.

Last checked on CASA Version 4.5.2