View source for Jupiter: continuum polarization calibration 5.5.0

{| class="wikitable"
|-
| style="background: red; color: white;" | '''DISCLAIMER'''
|-
| This is an advanced reference guide to calibration and imaging of pre-upgrade VLA polarimetric data with CASA 5.3.0. If you are a beginning or novice user, please consult relevant [https://casaguides.nrao.edu/index.php/Karl_G._Jansky_VLA_Tutorials CASA guides] first.
|}


<p style="color: #ff0000;"><b> Still under construction.. </b></p>



== Data Import ==

The data set for this tutorial lives [https://casa.nrao.edu/Data/VLA/Planets6cm/ here] (129MB). This is a VLA 6cm dataset that was observed back in 1999 to set the flux scale for calibration of the VLA. Included in the program were observations of the planets, including Jupiter which we will focus on in this tutorial. These are D-configuration data, with resolution of around 14". <p style="color: #ff0000;"><b> Need more reliable permanent location! </b></p>


Import the data into CASA. Task <tt>importuvfits()</tt> will read the original AIPS friendly UVFITS format and create a CASA native MS.

<source lang="python">
importuvfits(fitsfile='planets_6cm.fits', vis='jupiter6cm.demo.ms', antnamescheme='old')
</source>


Check the observation set up and print verbose summary of the observations to the CASA logger

<source lang="python">
listobs(vis='jupiter6cm.demo.ms')
</source>


The output of this task is fairly long, the abridged version is shown below since we may need some of this information later on during our calibration.

<pre style="background-color: #fffacd;">
##########################################
##### Begin Task: listobs            #####
   Observer: FLUX99     Project:   
Observation: VLA
Computing scan and subscan properties...
Data records: 2021424       Total elapsed time = 85136.5 seconds
   Observed from   15-Apr-1999/23:15:25.0   to   16-Apr-1999/22:54:21.6 (TAI)

[...]

Fields: 13
  ID   Code Name                RA               Decl           Epoch   SrcId      nRows
  0    A    0137+331            01:37:41.299500 +33.09.35.13400 J2000   0          72946
  1    T    0813+482            08:13:36.051800 +48.13.02.26200 J2000   1         227524
  2    A    0542+498            05:42:36.137900 +49.51.07.23400 J2000   2         227058
  3    T    0437+296            04:37:04.174700 +29.40.15.13600 J2000   3         113424
  4         VENUS               04:06:54.109428 +22.30.35.90609 J2000   4         121098
  5    A    0521+166            05:21:09.886000 +16.38.22.05200 J2000   5          81258
  6    T    1411+522            14:11:20.647700 +52.12.09.14100 J2000   6         243608
  7    A    1331+305            13:31:08.288100 +30.30.32.96000 J2000   7         307958
  8         MARS                14:21:41.365747 -12.21.49.45444 J2000   8         118570
  9         NGC7027             21:07:01.593000 +42.14.10.18600 J2000   9         136082
  10        NEPTUNE             20:26:01.136316 -18.54.54.21127 J2000   10        157736
  11        URANUS              21:15:42.828572 -16.35.05.59272 J2000   11         99412
  12        JUPITER             00:55:34.043951 +04.45.44.70633 J2000   12        114750

Spectral Windows:  (2 unique spectral windows and 1 unique polarization setups)
SpwID  Name   #Chans   Frame   Ch0(MHz)  ChanWid(kHz)  TotBW(kHz) CtrFreq(MHz)  Corrs          
  0      none       1   TOPO    4885.100     50000.000     50000.0   4885.1000   RR  RL  LR  LL
  1      none       1   TOPO    4835.100     50000.000     50000.0   4835.1000   RR  RL  LR  LL

Sources: 26
  ID   Name                SpwId RestFreq(MHz)  SysVel(km/s) 
  0    0137+331            0     0              0            
  0    0137+331            1     0              0            
  1    0813+482            0     0              0            
  1    0813+482            1     0              0            
  2    0542+498            0     0              0            
  2    0542+498            1     0              0            
  3    0437+296            0     0              0            
  3    0437+296            1     0              0            
  4    VENUS               0     0              0            
  4    VENUS               1     0              0            
  5    0521+166            0     0              0            
  5    0521+166            1     0              0            
  6    1411+522            0     0              0            
  6    1411+522            1     0              0            
  7    1331+305            0     0              0            
  7    1331+305            1     0              0            
  8    MARS                0     0              0            
  8    MARS                1     0              0            
  9    NGC7027             0     0              0            
  9    NGC7027             1     0              0            
  10   NEPTUNE             0     0              0            
  10   NEPTUNE             1     0              0            
  11   URANUS              0     0              0            
  11   URANUS              1     0              0            
  12   JUPITER             0     0              0            
  12   JUPITER             1     0              0            

Antennas 27
[....]

##### End Task: listobs              #####
##########################################
</pre>


Check also the array configuration and pick a reference antenna. Here, we will use antenna '11' as a reference since it's located in the middle of the array.

<source lang="python">
plotants(vis='jupiter6cm.demo.ms', figfile='jupiter6cm.demo.ant.png')
</source>



== Data Inspection and Editing ==

To inspect and interactively flag bad data use task <tt>plotms()</tt>. <b>Note</b>: it is not recommended to use <tt>plotms()</tt> to interactively flag the new post-upgrade VLA data due to their large volume and large bandwidths. However, the old VLA data are much smaller and <tt>plotms()</tt> is capable of managing it interactively.

First, inspect the primary flux and polarisation angle calibrator, 1331+305 (3C286). Inspect RR,LL and RL,LR correlations separately due to large amplitude differences between them (when all correlations are plotted together, the RL,LR will seem like bad data due to their much lower amplitudes as compared to RR,LL). Use <tt>Mark Regions</tt> within the <tt>plotms()</tt> GUI to draw boxes around points to flag, and hit <tt>Flag</tt> to flag. 

<source lang="python">
plotms(vis='jupiter6cm.demo.ms', selectdata=True, field='1331+305', correlation='RR,LL', xaxis='uvdist', yaxis='amp')
</source>

Now, inspect the other two sources we are interested in, the polarisation leakage calibrator (0137+331) and our target source (JUPITER).


Flagging can also be done non-interactively as per currently recommended CASA practise. Use task <tt>flagdata()</tt> to do so. For example, from inspecting our data in <tt>plotms()</tt> you should have noticed that Antenna 9 (ID=8) in spw 1 is often bad. The bad data on Antenna 9  are in the last 4 scans in spw='1' for the 0137+331 calibrator and for our target source (JUPITER), as well as in the last scan for all antennas in both spws on the target source. We can flag these points with the following <tt>flagdata()</tt> task executions.

<source lang="python">
flagdata(vis='jupiter6cm.demo.ms', mode='manual', field='0137+331', spw='1', antenna='9', timerange='42:00:00~48:00:00')
flagdata(vis='jupiter6cm.demo.ms', mode='manual', field='JUPITER', spw='1', antenna='9', timerange='16:26:00~22:20:00')
flagdata(vis='jupiter6cm.demo.ms', mode='manual', field='JUPITER', spw='', timerange='21:40:00~22:20:00')
</source>


There are more data points to flag -- keep flagging until you are happy with the results. The following commands will get rid of most bad data, but depending on how clean the data you want to proceed with, you may still want to inspect them in <tt>plotms()</tt> and flag interactively the reminder of bad datapoints.

<source lang="python">
flagdata(vis='jupiter6cm.demo.ms',mode='clip',field='1331+305',correlation='RR,LL',clipoutside=False,clipminmax=[0,0.75])
flagdata(vis='jupiter6cm.demo.ms',mode='clip',field='1331+305',correlation='RL,LR',clipoutside=False,clipminmax=[0,0.04])
flagdata(vis='jupiter6cm.demo.ms',mode='clip',field='0137+331',correlation='RR,LL',clipoutside=False,clipminmax=[0,0.55])
flagdata(vis='jupiter6cm.demo.ms',mode='clip',field='0137+331',correlation='RL,LR',clipoutside=False,clipminmax=[0,0.01])
</source>
If you are unfamiliar with flagging in CASA, consult detailed topical guide [https://casaguides.nrao.edu/index.php/VLA_CASA_Flagging-CASA5.0.0 Flagging VLA Data].



== Calibration ==

=== Set the Flux Density Scale ===

Set the absolute flux density scale, but only for Stokes I at the moment (total flux density model). Our primary flux calibrator here is 1331+305 (3C286). The default model for CASA 5.3+ is 'Perley-Butler 2017'. 

<source lang="python">
setjy(vis='jupiter6cm.demo.ms', field='1331+305', model='3C286_C.im')
</source>


=== Initial gain calibration ===

At this stage the data have an overall flux density scaling determined, but full gain solutions aren't there yet. The relevant task is <tt>gaincal()</tt> (analogous to the AIPS task CALIB). <tt>Gaincal()</tt> will produce a separate table with solutions, and we will use appropriate extensions to keep track of what is what.

<b>NOTE</b>: Since we have only two single-channel continuum spw, we do not want to do <i>bandpass calibration</i>. 

Firstly, generate an antenna zenith-angle dependent VLA gain curve calibration table

<source lang="python">
gencal(vis='jupiter6cm.demo.ms', caltable='jupiter6cm.demo.gc', caltype='gc')
</source>

Now, solve for antenna gains on 1331+305 and 0137+331, using the generated gain curve table (.gc).

<source lang="python">
gaincal(vis='jupiter6cm.demo.ms', caltable='jupiter6cm.demo.G', field='1331+305,0137+331', spw='', gaintype='G', calmode='ap', solint='inf', combine='', refant='11', minsnr=3, gaintable=['jupiter6cm.demo.gc'], parang=False)

# And check the solutions
plotcal(caltable='jupiter6cm.demo.G', xaxis='time', yaxis='amp', subplot=333, iteration='antenna')
plotcal(caltable='jupiter6cm.demo.G', xaxis='time', yaxis='phase', plotrange=[-1,-1,-200,200], subplot=333, iteration='antenna')
</source>

If all looks good, bootstrap the flux density scale of the flux calibrator onto the phase calibrators. AIPS called it GETJY, but CASA calls it <tt>fluxscale()</tt>. 

<source lang="python">
myFluxscale = fluxscale(vis='jupiter6cm.demo.ms', caltable='jupiter6cm.demo.G', fluxtable='jupiter6cm.demo.Gflx', reference='1331+305', transfer='0137+331', incremental=True, append=False, display=False)
</source>

The output is displayed in the logger as well as stored in the myFluxscale python dictionary.
<pre style="background-color: #fffacd;">
 Beginning fluxscale--(MSSelection version)-------
 Found reference field(s): 1331+305
 Found transfer field(s):  0137+331
 Flux density for 0137+331 in SpW=0 (freq=4.8851e+09 Hz) is: 5.29665 +/- 0.00449217 (SNR = 1179.09, N = 54)
 Flux density for 0137+331 in SpW=1 (freq=4.8351e+09 Hz) is: 5.34890 +/- 0.00176819 (SNR = 3025.07, N = 54)
 Fitted spectrum for 0137+331 with fitorder=1: Flux density = 5.32271 +/- 1.70229e-07 (freq=4.86004 GHz) spidx=-0.954124 (degenerate)
 Storing result in jupiter6cm.demo.Gflx
 Writing solutions to table: jupiter6cm.demo.Gflx
</pre>


When executing <tt>fluxscale()</tt>, the calibration table with the extension .G is modified and stored as a new table with the extension .Gflx. So far, solutions have been generated only for the calibrators, and have not yet been transferred to the target source(s). 

Before proceeding, inspect the flux density calibration and save results to a file.

<source lang="python">
plotcal(caltable='jupiter6cm.demo.Gflx', xaxis='time', yaxis='amp', showgui=True, figfile='jupiter6cm.demo.Gflx.amp.png')

plotcal(caltable='jupiter6cm.demo.Gflx', xaxis='time', yaxis='phase', plotrange=[-1,-1,-200,200], showgui=True, figfile='jupiter6cm.demo.Gflx.phase.png')
</source>



=== Polarisation calibration ===


==== Set the polarisation model ====

Now, set the polarisation model for the polarised position-angle calibrator (here 1331+305=3C286 which is also our primary flux calibrator). For polarisation properties of your primary polarisation calibrator see [https://science.nrao.edu/facilities/vla/docs/manuals/obsguide/modes/pol#section-4 VLA Polarimetry Guide].

<source lang="python">
i0=7.3109    # Stokes I value for spw 0 ch 0
f0=4.8851    # Frequency for spw0 ch0 (note that in our data the 'lower' spw is actually higher frequency)
alpha=log(i0/7.35974932)/log(4.8351/f0) # Values from our setjy() run on Stokes I earlier
c0=0.114     # Fractional polarisation 11.4% for 5GHz
d0=33*pi/180 # Polarisation angle 33deg in radians

myPolSetjy = setjy(vis='jupiter6cm.demo.ms', field='1331+305', standard='manual', spw='0', fluxdensity=[i0,0,0,0], spix=[alpha,0], reffreq=str(f0)+'GHz', polindex=[c0,0], polangle=[d0,0], scalebychan=True, usescratch=False)
</source>

The results are displayed in the CASA logger as well as saved in the myPolSetjy python dictionary

<pre style="background-color: #fffacd;">
#In CASA
CASA <49>: myPolSetjy
Out[49]: 
{'7': {'0': {'fluxd': array([ 7.3109    ,  0.33899165,  0.7613877 ,  0.        ])},
  'fieldName': '1331+305'},
 'format': "{field Id: {spw Id: {fluxd: [I,Q,U,V] in Jy}, 'fieldName':field name }}"}
</pre>



==== Solve for the leakage terms (D terms) ====

Solve for polarization leakage on 0137+331. In this step we solve for the instrumental polarization. We will assume here our calibrator has unknown polarisation (poltype='D+QU' if good parallactic coverage, 'D' otherwise, consult [https://casa.nrao.edu/docs/taskref/polcal-task.html <tt>polcal()</tt> help] for more information on options). 

<source lang="python">
polcal(vis='jupiter6cm.demo.ms', caltable='jupiter6cm.demo.D', field='0137+331', spw='', refant='11', poltype='D+QU', solint='inf', combine='scan',minsnr=3, gaintable=['jupiter6cm.demo.gc', 'jupiter6cm.demo.G'], gainfield=['','0137+331',''])
</source>

Check the solutions

<source lang="python">
plotcal(caltable='jupiter6cm.demo.D', xaxis='antenna', yaxis='amp', showgui=True, figfile='jupiter6cm.demo.D.amp.png')

plotcal(caltable='jupiter6cm.demo.D', xaxis='antenna', yaxis='phase', plotrange=[-1,-1,-200,200], showgui=True, figfile='jupiter6cm.demo.D.phase.png')

plotcal(caltable='jupiter6cm.demo.D', xaxis='antenna', yaxis='snr', showgui=True, figfile='jupiter6cm.demo.D.snr.png')
</source>



==== Solve for the R-L polarisation angle (X term) ====

The total polarisation is now correct (since we just calibrated the instrumental polarisation, i.e. D terms), but now the R-L phase needs to be calibrated to obtain an accurate polarisation position angle. 

<source lang="python">
polcal(vis='jupiter6cm.demo.ms', caltable='jupiter6cm.demo.X', field='1331+305', spw='', refant='11', poltype='Xf', solint='inf', combine='scan',minsnr=3, gaintable=['jupiter6cm.demo.gc', 'jupiter6cm.demo.G', 'jupiter6cm.demo.D'], gainfield=['','1331+305','',''])
</source>

Check the solutions in CASA logger window

<pre style="background-color: #fffacd;">
The following calibration term is arranged for solve:
.   Xf Jones: table=jupiter6cm.demo.X append=false solint=inf,none refantmode='flex' refant='none' minsnr=3 apmode=AP solnorm=false
For solint = inf, found 2 solution intervals.
Mean position angle offset solution for 1331+305 (spw = 0) = 80.7525 deg.
Mean position angle offset solution for 1331+305 (spw = 1) = -12.0478 deg.
  Found good Xf Jones solutions in 2 solution intervals.
</pre>

<p style="color: #ff0000;"><b> something's broken here, can't run poltype='X' getting SEVERE error then </b></p>



=== Apply the calibration ===

Now that we have derived all the calibration solutions and saved them in the tables of multiple extensions, we need to apply them to the actual data. The following tasks will apply the solution tables to the DATA and write a new column CORRECTED_DATA as it is standard for CASA. Important: make sure you set <tt>parang=True</tt>.


<source lang="python">
applycal(vis='jupiter6cm.demo.ms', field='1331+305', spw='', selectdata=False, gaintable=['jupiter6cm.demo.gc', 'jupiter6cm.demo.G', 'jupiter6cm.demo.D','jupiter6cm.demo.X'], gainfield=['','1331+305'], calwt=[False], parang=True)

applycal(vis='jupiter6cm.demo.ms', field='0137+331', spw='', selectdata=False, gaintable=['jupiter6cm.demo.gc', 'jupiter6cm.demo.G', 'jupiter6cm.demo.Gflx','jupiter6cm.demo.D','jupiter6cm.demo.X'], gainfield=['','0137+331','0137+331'], calwt=[False], parang=True)

applycal(vis='jupiter6cm.demo.ms', field='JUPITER', spw='', selectdata=False, gaintable=['jupiter6cm.demo.gc', 'jupiter6cm.demo.G', 'jupiter6cm.demo.Gflx','jupiter6cm.demo.D','jupiter6cm.demo.X'], gainfield=['','0137+331','0137+331'], calwt=[False], parang=True)
</source>


It's a good idea to split the Jupiter target data at this point. We will write the corrected data of our target source to a new single-source MS.

<source lang="python">
split(vis='jupiter6cm.demo.ms', outputvis='jupiter6cm.demo.JUPITER.split.ms', field='JUPITER', datacolumn='corrected')
</source>

You can use <tt>plotms()</tt> task to look at the split calibrated data.


== Initial imaging (Stokes I) ==

<p style="color: #ff0000;"><b> TBA </b></p>



== Self-calibration ==

<p style="color: #ff0000;"><b> TBA </b></p>



== Full Stokes Imaging ==

We will now image and clean the calibrated Jupiter data. The recommended task for deconvolution is <tt>tclean()</tt>.

<source lang="python">
tclean(vis='jupiter6cm.demo.JUPITER.split.ms', stokes='IQUV', field='', spw='', datacolumn='data', imagename='jupiter6cm.demo.JUPITER.IQUV.clean', imsize=[288], cell=['3arcsec'], specmode='mfs', gridder='standard', deconvolver='clarkstokes', weighting='briggs', threshold=5e-05, mask='', niter=10000, interactive=True, cycleniter=100)
</source>


The <tt>tclean()</tt> task is new for CASA 5.3. However, if you are performing this data reduction in earlier versions of CASA, or just want to use the standard <tt>clean()</tt> here is how you would run it. Note though, that <tt>clean()</tt> will disappear in the future versions of CASA with <tt>tclean()</tt> becoming the only task to perform this step. Both <tt>tclean()</tt> and <tt>clean()</tt> should give you similar results here. 

Note: since we set mask="" in our cleaning tasks, the interactive GUI will not allow you to do the deconvolution before you draw the mask on at least one plane. When drawing a mask, make sure to extend it to all channels and all polarisations.

<source lang="python">
clean(vis='jupiter6cm.demo.JUPITER.split.ms', stokes='IQUV', field='', spw='', imagename='jupiter6cm.demo.JUPITER.IQUV.oldclean', imsize=[288], cell=['3arcsec'], mode='mfs', psfmode='clarkstokes', imagermode='csclean', weighting='briggs', threshold='0.05mJy', mask='', niter=10000, interactive=True, npercycle=100)
</source>

Inspect the quality of the cleaned images with <tt>imstat()</tt>, but remember that in this example we did not correct for the primary beam. 


If you are interested in performing self-calibration, consult either the [https://casaguides.nrao.edu/index.php/VLA_5_GHz_continuum_survey_of_Seyfert_galaxies pre-upgrade VLA continuum tutorial] or the [https://casaguides.nrao.edu/index.php/VLA_Continuum_Tutorial_3C391-CASA5.0.0#Self-Calibration JVLA CASA continuum tutorial].



== Polarisation Intensity and Angle Images  ==


We now want to save each Stokes plane as separate images. We can use either the <tt>imsubimage()</tt> or <tt>immath()</tt> tasks to do so. 

Run 

<source lang="python">
imsubimage(imagename='jupiter6cm.demo.JUPITER.IQUV.clean.image',outfile='JUPITER.I.image',stokes='I')
imsubimage(imagename='jupiter6cm.demo.JUPITER.IQUV.clean.image',outfile='JUPITER.Q.image',stokes='Q')
imsubimage(imagename='jupiter6cm.demo.JUPITER.IQUV.clean.image',outfile='JUPITER.U.image',stokes='U')
</source>

or 

<source lang="python">
immath(imagename='jupiter6cm.demo.JUPITER.IQUV.clean.image', mode='evalexpr', stokes='I', expr='IM0', outfile='JUPITER.I.image')
immath(imagename='jupiter6cm.demo.JUPITER.IQUV.clean.image', mode='evalexpr', stokes='Q', expr='IM0', outfile='JUPITER.Q.image')
immath(imagename='jupiter6cm.demo.JUPITER.IQUV.clean.image', mode='evalexpr', stokes='U', expr='IM0', outfile='JUPITER.U.image')
</source>



Finally, we want to create the polarization intensity and position angle images. We reached rms of approx 2.5 mJy/bm in our I and Q images, and ~1mJy/bm in U, what resulted in rms level of approx 1.7 mJy/bm in our polarisation intensity image (as you can measure from POLI image created below). We will use the <tt>polithresh</tt> parameter when creating the polarisation angle POLA image to get rid of the noise.

<source lang="python">
immath(imagename=['JUPITER.Q.image','JUPITER.U.image'], mode='poli', outfile='JUPITER.POLI.image')
immath(imagename=['JUPITER.Q.image','JUPITER.U.image'], mode='pola', outfile='JUPITER.POLA.image',polithresh='10mJy/beam')
</source>

Again, check the statistics of these final images with <tt>imstat()</tt> and/or <tt>viewer()</tt>.



And that's it. For the tutorial on how to perform analysis, display and manipulation of the polarisation images now, consult the appropriate [https://casaguides.nrao.edu/index.php/VLA_Continuum_Tutorial_3C391-CASA5.0.0 VLA CASA guides].

<p style="color: #ff0000;"><b>Will add final image here (POLI img with I contours and POLA vectors)</b></p>



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