Difference between revisions of "EVLA high frequency spectral line tutorial - IRC+10216 - calibration"

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(Gain Calibration)
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Then, you can calibrate the target source using the phase and amplitude solutions you have obtained.  Apply the target source calibration solutions using {{applycal}}.  Use {{plotms}} to examine the calibrated data.  If more flagging is required, redo all calibration steps.  When the data look good, {{split}} the target source into a separate measurement set.  Subtract the continuum flux using {{uvcontsub}}.  Make Doppler corrections using {{cvel}}, or let {{clean}} do the Doppler corrections on the fly.
 
Then, you can calibrate the target source using the phase and amplitude solutions you have obtained.  Apply the target source calibration solutions using {{applycal}}.  Use {{plotms}} to examine the calibrated data.  If more flagging is required, redo all calibration steps.  When the data look good, {{split}} the target source into a separate measurement set.  Subtract the continuum flux using {{uvcontsub}}.  Make Doppler corrections using {{cvel}}, or let {{clean}} do the Doppler corrections on the fly.
 
==Applycal and Inspect==
 
 
Now we apply the calibration to each source, according to which tables are appropriate, and which source should be used to do that particular calibration. For the calibrators, all bandpass solutions come from the bandpass calibrator (id=5), and the phase and amplitude calibration comes from their own solutions.
 
 
'''Note:''' In applycal we set calwt=F. It is very important to turn off this parameter which determines if the weights are calibrated along with the data. Data from antennas with better receiver performance and/or longer integration times should have higher weights, and it can be advantageous to factor this information into the calibration. During the VLA era, meaningful weights were available for each visibility. However, EVLA is not yet recording the information necessary to calculate meaningful weights. Since these data weights are used at the imaging stage you can get strange results from having calwt=T when the input weights are themselves not meaningful, especially for self-calibration on resolved sources (your flux calibrator and target for example). In a few months EVLA data will again have meaningful weights and the default calwt=T will likely again be the best option.
 
 
<source lang="python">
 
# In CASA
 
# for the gain/phase calibrator (field '2'):
 
applycal(vis=vis,field='2',
 
        gaintable=['bandpass.bcal','intphase.gcal','flux.cal'],
 
        gainfield=['','5','2','2'],
 
        gaincurve=T,calwt=F)
 
</source>
 
 
<source lang="python">
 
# In CASA
 
# for the bandpass calibrator (field '5'):
 
applycal(vis=vis,field='5',
 
        gaintable=['bandpass.bcal','intphase.gcal','flux.cal'],
 
        gainfield=['','5','5','5'],
 
        gaincurve=T,calwt=F)
 
</source>
 
 
<source lang="python">
 
# In CASA
 
# for the flux calibrator (field '7'):
 
applycal(vis=vis,field='7',
 
        gaintable=['bandpass.bcal','intphase.gcal','flux.cal'],
 
        gainfield=['','5','7','7'],
 
        gaincurve=T,calwt=F)
 
</source>
 
 
For the target we apply the bandpass from id=5, and the calibration from the gain calibrator (id=2):
 
 
<source lang="python">
 
# In CASA
 
# for the target source IRC10216 (field '3'):
 
applycal(vis=vis,field='3',
 
        gaintable=['bandpass.bcal','scanphase.gcal','flux.cal'],
 
        gainfield=['','5','2','2'],
 
        gaincurve=T,calwt=F)
 
</source>
 
 
Now inspect the corrected data:
 
[[Image:applycal_inspect.png|thumb|Plot of calibrated amplitudes over time.]]
 
<source lang="python">
 
# In CASA
 
plotms(vis=vis,field='5',ydatacolumn='corrected',
 
      xaxis='time',yaxis='amp',correlation='RR,LL',
 
      avgchannel='64',spw='0:4~60',antenna='', coloraxis='antenna1')
 
</source>
 
 
This plot shows some data deviating from the average amplitudes. Use methods described above to
 
mark a region for a small number of deviant data points, and click "Locate". You will find that ea12 is responsible.
 
 
<source lang="python">
 
# In CASA
 
plotms(vis=vis,field='2',ydatacolumn='corrected',
 
      xaxis='time',yaxis='amp',correlation='RR,LL',
 
      avgchannel='64',spw='0:4~60',antenna='', coloraxis='antenna2')
 
</source>
 
 
Here we see some problems, with high points. Mark some regions
 
and locate in {{plotms}} to find out which antennas and in which spws. Pay special
 
attention to antennas that have been called out already as showing some dubious behavior.
 
 
What you find is that ea07 which we flagged spw=1 above, is also bad for the same timerange in spw=0. This was not obvious in the raw data, because spw=0 was adjusted in the on-line system by a gain attenuator, while spw=1 wasn't. So a lack of power on this antenna can look like very low (and obvious) amplitudes in spw=1 but not for spw=0. Looking carefully you'll see that ea07 is actually pretty noisy throughout.
 
[[Image:ea12.png|thumb|Plot of antenna ea12 by itself]]
 
[[Image:ea23.png|thumb|Plot of antenna ea23 by itself]]
 
 
From the locate we also find that ea12 and ea23 show some high points; to see this, replot baselines using each of them alone:
 
 
<source lang="python">
 
plotms(vis=vis,field='2',ydatacolumn='corrected',
 
      xaxis='time',yaxis='amp',correlation='RR,LL',
 
      avgchannel='64',spw='0:4~60',antenna='ea12', coloraxis='antenna2')
 
</source>
 
 
<source lang="python">
 
plotms(vis=vis,field='2',ydatacolumn='corrected',
 
      xaxis='time',yaxis='amp',correlation='RR,LL',
 
      avgchannel='64',spw='0:4~60',antenna='ea23', coloraxis='antenna2')
 
</source>
 
 
it may be a a good idea to flag ea12 completely - it's just a bit noisy all around and ea23 is pretty noisy during the first scans between initial and second pointing. Recall that these are antennas we became suspicious of while inspecting the calibration solutions.
 
 
[[Image:target_uvdist.png|thumb|IRC+10216 as a function of uv-distance.]]
 
Now lets see how the target looks. Because the target has resolved structure, its best to look at it as
 
a function of uvdistance. We'll go ahead and exclude the three antennas we already know have problems.
 
 
<source lang="python">
 
# In CASA
 
plotms(vis=vis,field='3',ydatacolumn='corrected',
 
      xaxis='uvdist',yaxis='amp',correlation='RR,LL',
 
      avgchannel='64',spw='0:4~60',antenna='!ea07;!ea12;!ea23', coloraxis='antenna2')
 
</source>
 
 
you can see that the spikes
 
are caused by a single antenna. Use, zoom, mark, and locate to see which one.
 
Also look at spw=1.
 
 
Turns out to be ea28; to confirm, replot with antenna=!ea28, and
 
 
<source lang="python">
 
# In CASA
 
plotms(vis=vis,field='3',ydatacolumn='corrected',
 
      xaxis='uvdist',yaxis='amp',correlation='RR,LL',
 
      avgchannel='64',spw='0:4~60',antenna='!ea07;!ea12;!ea23;!ea28', coloraxis='antenna2')
 
</source>
 
 
To see if it's restricted to a certain time
 
 
<source lang="python">
 
# In CASA
 
plotms(vis=vis,field='3',ydatacolumn='corrected',
 
      xaxis='time',yaxis='amp',correlation='RR,LL',
 
      avgchannel='64',spw='0:4~60',antenna='ea28', coloraxis='antenna1')
 
</source>
 
 
 
Baselines with ea28 clearly show issues until about two-thirds of the way through the observation.
 
Plot another distant antenna to compare. We will go ahead and flag it all, since its hanging far out on the north
 
arm by itself.
 
 
The additional data we've identified as bad need to be flagged, and then all the calibration steps will need to be run
 
again.
 
 
<source lang="python">
 
# In CASA
 
flagdata(vis=vis,
 
        field=['',''],
 
        spw=['',''],
 
        antenna=['ea07,ea12,ea28','ea07,ea23'],
 
        timerange=['','03:21:40~04:10:00'])
 
</source>
 
 
==Redo Calibration after more Flagging==
 
 
After flagging, you'll need to repeat the calibration steps above.  Here, we append  _redo to the table names to distinguish them from the first round, in case we want to compare with previous versions.
 
 
<source lang="python">
 
# In CASA
 
gaincal(vis=vis,caltable='bpphase_redo.gcal',
 
        field='5',spw='0~1:20~40',
 
        refant='ea02',calmode='p',solint='int',minsnr=2.0,
 
        gaincurve=T)
 
</source>
 
 
<source lang="python">
 
# In CASA
 
bandpass(vis=vis,caltable='bandpass_redo.bcal',
 
        field='5',
 
        refant='ea02',solint='inf',solnorm=T,
 
        gaintable=['bpphase_redo.gcal'],
 
        gaincurve=T)
 
</source>
 
 
<source lang="python">
 
# In CASA
 
gaincal(vis=vis,caltable='intphase_redo.gcal',
 
        field='2,5,7',spw='0~1:4~60',
 
        refant='ea02',calmode='p',solint='int',minsnr=2.0,
 
        gaintable=['bandpass_redo.bcal'],
 
        gaincurve=T)
 
</source>
 
 
<source lang="python">
 
# In CASA
 
gaincal(vis=vis,caltable='scanphase_redo.gcal',
 
        field='2,5,7',spw='0~1:4~60',
 
        refant='ea02',calmode='p',solint='inf',minsnr=2.0,
 
        gaintable=['bandpass_redo.bcal'],
 
        gaincurve=T)
 
</source>
 
 
<source lang="python">
 
# In CASA
 
gaincal(vis=vis,caltable='amp_redo.gcal',
 
        field='2,5,7',spw='0~1:4~60',
 
        refant='ea02',calmode='ap',solint='inf',minsnr=2.0,
 
        gaintable=['bandpass_redo.bcal','intphase_redo.gcal'],
 
        gaincurve=T)
 
</source>
 
 
<source lang="python">
 
# In CASA     
 
fluxscale(vis=vis,caltable='amp_redo.gcal',
 
          fluxtable='flux_redo.cal',reference='7')
 
</source>
 
 
<pre style="background-color: #fffacd;">
 
Flux density for J0954+1743 in SpW=0 is: 0.235345 +/- 0.000879422
 
    (SNR = 267.613, nAnt= 16)
 
Flux density for J0954+1743 in SpW=1 is: 0.241996 +/- 0.000930228
 
    (SNR = 260.147, nAnt= 16)
 
Flux density for J1229+0203 in SpW=0 is: 25.2479 +/- 0
 
    (SNR = inf, nAnt= 16)
 
Flux density for J1229+0203 in SpW=1 is: 24.9907 +/- 0
 
    (SNR = inf, nAnt= 16)
 
 
Flux density for J0954+1743 in SpW=0 is: 0.247052 +/- 0.000946345 (SNR = 261.059, nAnt= 16)
 
Flux density for J0954+1743 in SpW=1 is: 0.254038 +/- 0.00097531 (SNR = 260.47, nAnt= 16)
 
Flux density for J1229+0203 in SpW=0 is: 26.5079 +/- 0 (SNR = inf, nAnt= 16)
 
Flux density for J1229+0203 in SpW=1 is: 26.2335 +/- 0 (SNR = inf, nAnt= 16)
 
</pre>
 
 
Feel free to pause here and remake the calibration solution plots from above, just be sure to put in the revised table names.
 
 
==Redo Applycal and Inspect==
 
 
Now, apply all the new calibrations, which will overwrite the old ones.  These commands are identical to those above, with the exception of the _redo part of each calibration filename.
 
 
<source lang="python">
 
# In CASA
 
applycal(vis=vis,field='2',
 
        gaintable=['bandpass_redo.bcal','intphase_redo.gcal','flux_redo.cal'],
 
        gainfield=['','5','2','2'],
 
        gaincurve=T,calwt=F)
 
</source>
 
 
<source lang="python">
 
# In CASA
 
applycal(vis=vis,field='5',
 
        gaintable=['bandpass_redo.bcal','intphase_redo.gcal','flux_redo.cal'],
 
        gainfield=['','5','5','5'],
 
        gaincurve=T,calwt=F)
 
</source>
 
 
<source lang="python">
 
# In CASA
 
applycal(vis=vis,field='7',
 
        gaintable=['bandpass_redo.bcal','intphase_redo.gcal','flux_redo.cal'],
 
        gainfield=['','5','7','7'],
 
        gaincurve=T,calwt=F)
 
</source>
 
 
[[Image:gaincal_corrflag.png|thumb|Gain calibrator after further flagging and recalibration]]
 
[[Image:target_corrflag.png|thumb|IRC+10216 after further flagging and recalibration (after selecting colorize by spw).]]
 
 
<source lang="python">
 
# In CASA
 
applycal(vis=vis,field='3',
 
        gaintable=['bandpass_redo.bcal','scanphase_redo.gcal','flux_redo.cal'],
 
        gainfield=['','5','2','2'],
 
        gaincurve=T,calwt=F)
 
</source>
 
 
Now you can inspect the calibrated data again. Except for random scatter things look pretty good.
 
 
<source lang="python">
 
# In CASA
 
plotms(vis=vis,field='2',ydatacolumn='corrected',
 
      xaxis='time',yaxis='amp',correlation='RR,LL',
 
      avgchannel='64',spw='0:4~60',antenna='', coloraxis='antenna2')
 
</source>
 
 
You can use the Mark and Locate buttons to assess that the remaining scatter seems random, i.e. no particular antenna or time range appears to be responsible.
 
 
<source lang="python">
 
# In CASA
 
plotms(vis=vis,field='3',ydatacolumn='corrected',
 
      xaxis='uvdist',yaxis='amp',correlation='RR,LL',
 
      avgchannel='64',spw='0~1:4~60',antenna='', coloraxis='spw')
 
</source>
 
 
==Split==
 
 
Now we split the data into individual files. This is not strictly necessary, as you can select the appropriate fields in later clean stages, but it is safer in case for example you get confused with later processing and want to fall back to this point (this is especially a good idea if you plan to do continuum subtraction or self calibration later on). It also makes smaller individual files in case you want to copy to another machine or colleague.
 
 
Here, we split off the data for the phase calibrator and the target:
 
 
<source lang="python">
 
# In CASA
 
split(vis=vis,outputvis='J0954',
 
      field='2')
 
</source>
 
 
<source lang="python">
 
# In CASA
 
split(vis=vis,outputvis='IRC10216',
 
      field='3')
 
</source>
 
 
To reinitialize the scratch columns for use by later tasks, we need to run clearcal for both new datasets
 
 
<source lang="python">
 
# In CASA
 
clearcal(vis='J0954')
 
</source>
 
 
<source lang="python">
 
# In CASA
 
clearcal(vis='IRC10216')
 
</source>
 
 
This concludes the calibration phase of the data reductions. The tutorial continues with continuum subtraction, imaging, and image analysis in
 
[[EVLA high frequency spectral line tutorial - IRC+10216 - imaging]].
 
  
 
[[Main Page | &#8629; '''CASAguides''']]
 
[[Main Page | &#8629; '''CASAguides''']]

Revision as of 15:55, 6 January 2012

This page is under development

This CASA Guide provides a simplified approach to the editing, calibration, and imaging of a galactic EVLA observation for use in NRAO Community Day Events. For a complete description of the data reduction process see EVLA high frequency Spectral Line tutorial - IRC+10216 part1 and EVLA high frequency Spectral Line tutorial - IRC+10216 part2.

Overview

VLT V-band image of IRC+10216 showing dust rings out to a radius of 90" by Leão et al. (2006, A&A, 455, 187).

This tutorial describes the data reduction for two spectral lines, HC3N and SiS, observed toward the AGB star IRC+10216 by the EVLA in D-configuration.

Getting the data

The data for this tutorial can be obtained by anonymous FTP from ftp://ftp.aoc.nrao.edu/staff/gvanmoor/community_day/. Download all 4 TAR files.

For example,

# In UNIX
wget 'ftp://ftp.aoc.nrao.edu/staff/gvanmoor/community_day/*'

Initial Inspection and Flagging

GEt a summary listing of the data set using listobs.

# In CASA
vis = 'day2_TDEM0003_20s_full'
listobs(vis=vis, verbose=True)

Below we have cut and pasted the most relevant output from the logger.

Fields: 4
  ID   Code Name                RA              Decl          Epoch   SrcId nVis
  2    D    J0954+1743          09:54:56.82363 +17.43.31.2224 J2000   2     32726
  3    NONE IRC+10216           09:47:57.38200 +13.16.40.6600 J2000   3     99540
  5    F    J1229+0203          12:29:06.69973 +02.03.08.5982 J2000   5     5436
  7    E    J1331+3030          13:31:08.28798 +30.30.32.9589 J2000   7     2736
   (nVis = Total number of time/baseline visibilities per field)
Spectral Windows:  (2 unique spectral windows and 1 unique polarization setups)
  SpwID  #Chans Frame Ch1(MHz)    ChanWid(kHz)  TotBW(kHz)  Corrs
  0          64 TOPO  36387.2295  125           8000        RR  RL  LR  LL
  1          64 TOPO  36304.542   125           8000        RR  RL  LR  LL
Sources: 10
  ID   Name                SpwId RestFreq(MHz)  SysVel(km/s)
  0    J1008+0730          0     0.03639232     -0.026
  0    J1008+0730          1     0.03639232     -0.026
  2    J0954+1743          0     0.03639232     -0.026
  2    J0954+1743          1     0.03639232     -0.026
  3    IRC+10216           0     0.03639232     -0.026
  3    IRC+10216           1     0.03639232     -0.026
  5    J1229+0203          0     0.03639232     -0.026
  5    J1229+0203          1     0.03639232     -0.026
  7    J1331+3030          0     0.03639232     -0.026
  7    J1331+3030          1     0.03639232     -0.026
Antennas: 19:
  ID   Name  Station   Diam.    Long.         Lat.
  0    ea01  W09       25.0 m   -107.37.25.2  +33.53.51.0
  1    ea02  E02       25.0 m   -107.37.04.4  +33.54.01.1
  2    ea03  E09       25.0 m   -107.36.45.1  +33.53.53.6
  3    ea04  W01       25.0 m   -107.37.05.9  +33.54.00.5
  4    ea05  W08       25.0 m   -107.37.21.6  +33.53.53.0
  5    ea07  N06       25.0 m   -107.37.06.9  +33.54.10.3
  6    ea08  N01       25.0 m   -107.37.06.0  +33.54.01.8
  7    ea09  E06       25.0 m   -107.36.55.6  +33.53.57.7
  8    ea12  E08       25.0 m   -107.36.48.9  +33.53.55.1
  9    ea15  W06       25.0 m   -107.37.15.6  +33.53.56.4
  10   ea19  W04       25.0 m   -107.37.10.8  +33.53.59.1
  11   ea20  N05       25.0 m   -107.37.06.7  +33.54.08.0
  12   ea21  E01       25.0 m   -107.37.05.7  +33.53.59.2
  13   ea22  N04       25.0 m   -107.37.06.5  +33.54.06.1
  14   ea23  E07       25.0 m   -107.36.52.4  +33.53.56.5
  15   ea24  W05       25.0 m   -107.37.13.0  +33.53.57.8
  16   ea25  N02       25.0 m   -107.37.06.2  +33.54.03.5
  17   ea27  E03       25.0 m   -107.37.02.8  +33.54.00.5
  18   ea28  N08       25.0 m   -107.37.07.5  +33.54.15.8

We summarize the observing strategy in this table.

Gain calibrator J0954+1743 field id = 2
Bandpass calibrator J1229+0203 field id = 5
Flux calibrator J1331+3030 (3C286) field id = 7
Science target IRC+10216 field id = 3
Antenna locations from running plotants

Create a plot of antenna positions using plotants.

Elevation as a function of time (after selecting colorize by field).
# In CASA
plotants(vis=vis)
Result of plotms
Zooming in and marking region (hatched box)

Next, let's look at all the source amplitudes as a function of time using plotms.

# In CASA
plotms(vis=vis, xaxis='time', yaxis='amp', correlation='RR,LL',
       avgchannel='64', spw='0:4~60', coloraxis='field')

Now zoom in on the region very near zero amplitude for sources J0954+1743 and IRC+10216. To zoom, select the Zoom tool in lower left corner of the plotms GUI, then you can left click to draw a box. Look for the low values (you may want to zoom a few times to really see the suspect points clearly). Now use the Mark Region and Locate buttons (located along the bottom of the GUI) to see which antenna is causing problems. The output will be shown in the logger. Since all the "located" baselines include ea12, this is the responsible antenna.

Now click the clear region button, and then go back to the zoom button to zoom in further to note exactly what the time range is: 03:41:00~04:10:00.

Check the other sideband by changing spw to 1:4~60. You will have to rezoom. If you have trouble, click on the Mark icon and then back to zoom. In spw=1, ea07 is bad from the beginning until after next pointing run: 03:21:40~04:10:00. To see this, compare the amplitudes when antenna is set to 'ea07' and when it is set to one of the other antennas, such as 'ea08'.

If you set antenna to 'ea12' and zoom in on this intial timerange, you can also see that ea12 is bad during the same time range as for spw 0. You can also see this by entering '!ea07' for antenna, which removes ea07 from the plot (in CASA selection, ! deselects).

We can flag the bad data using flagdata.

# In CASA
flagdata(vis=vis,
         field=['2,3','2,3'],
         spw=['','1'],
         antenna=['ea12','ea07'],
         timerange=['03:41:00~04:10:00','03:21:40~04:10:00'])

flagdata works by spanning up a matrix. The first entries in each list must be taken as one flagging command, as well as the second entries etc. Lists within lists are fine. In the above example, the first flagging command is issued for fields 2 and 3 for all spws and within the 03:41:00~04:10:00 timerange. A second command is again for the fields 2 and 3 but for spw 1 only and for the second timerange in the list '03:21:40~04:10:00'.

Note that because the chosen timerange is limited to fields 2 and 3, the field parameter is not really needed; however, flagdata will run fastest if you put as many constraints as possible.

Set Up the Model for the Flux Calibrator

We set the model for the flux calibrator using setjy. First, check the availability of calibration models.

# In CASA
setjy(vis=vis,listmodimages=T)

There is no Ka-band model of 3C286. We will use the K-band model instead.

# In CASA
setjy(vis=vis,field='7',spw='0~1',
      modimage='3C286_K.im')

setjy scales the total flux in the model image to that appropriate for your individual spectral window frequencies according to the calibrator's flux and reports this number to the logger.

The logger output for each spw is:
J1331+3030 (fld ind 7) spw 0  [I=1.7762, Q=0, U=0, V=0] Jy, (Perley-Butler 2010)
J1331+3030 (fld ind 7) spw 1  [I=1.7794, Q=0, U=0, V=0] Jy, (Perley-Butler 2010)

The absolute fluxes for the frequencies have now been determined and one can proceed to the bandpass and complex gain calibrations.

Bandpass

Correct the phase variations with time before solving for the bandpass to prevent decorrelation of the vector averaged bandpass solution.

Phase only calibration before bandpass. The 4 lines are both polarizations in both spw, unfortunately two of them get the same color green at the moment.
# In CASA
gaincal(vis=vis, caltable='bpphase.gcal', field='5', spw='0~1:20~40',
        refant='ea02', gaintype='G', calmode='p',solint='int',
        gaincurve=T)

Plot the solutions using plotcal.

# In CASA
plotcal(caltable='bpphase.gcal',xaxis='time',yaxis='phase',
        iteration='antenna',subplot=331,plotrange=[0,0,-180,180])

Next we can apply this phase solution on the fly while determining the bandpass solutions on the timescale of the bandpass calibrator scan (solint='inf').

# In CASA
bandpass(vis=vis, caltable='bandpass.bcal', field='5', refant='ea02',
         solint='inf', solnorm=T, gaintable='bpphase.gcal',
         gaincurve=T)

Plot the solutions, amplitude and phase:

Amplitude Bandpass solutions
Phase Bandpass solutions
# In CASA
plotcal(caltable='bandpass.bcal',xaxis='chan',yaxis='amp',
        iteration='antenna',subplot=331)


# In CASA
plotcal(caltable='bandpass.bcal',xaxis='chan',yaxis='phase',
        iteration='antenna',subplot=331)

Remaining Calibration

In this Community Day Event guide, we will skip over the remaining calibration steps. However, you can refer to EVLA high frequency Spectral Line tutorial - IRC+10216 part1 for full details. To summarize the missing steps, you bootstrap the flux densities of the secondary calibrators by

  1. doing a phase only calibration on all calibrators using gaincal,
  2. doing amplitude only calibration on all calibrators while applying the phase-only solutions, and
  3. deriving the flux density of the secondary calibrators while applying the previously obtained solutions.

Then, you can calibrate the target source using the phase and amplitude solutions you have obtained. Apply the target source calibration solutions using applycal. Use plotms to examine the calibrated data. If more flagging is required, redo all calibration steps. When the data look good, split the target source into a separate measurement set. Subtract the continuum flux using uvcontsub. Make Doppler corrections using cvel, or let clean do the Doppler corrections on the fly.

CASAguides

--Crystal Brogan --additions: Juergen Ott

Last checked on CASA Version 3.3.0.