EVLA high frequency spectral line tutorial - IRC+10216 - calibration
This page is under development
This tutorial describes the data reduction for two spectral lines observed toward the AGB star IRC+10216. In this EVLA OSRO1 mode observation one subband was observed in each of two basebands, the subbands were centered on the HC3N and SiS lines near 36 GHz. The raw data were loaded into CASA with importevla, where zero and shadowed data were flagged. Then the data were "split", so we could average from the native 1 second integrations to 20 seconds, select only antennas with Ka-band receivers, and select only spectral windows (called spw in CASA) with Ka-band data. This produces a significantly smaller dataset for processing.
IRC+10216 (CW Leo) is the brightest star in the sky at 5 microns but only 16th magnitude visually. It was discovered during the first survey of the infrared sky, carried out by Bob Leighton and Gerry Neugebauer in 1965. An Asymptotic Giant Branch star, it is a Mira-type variable going through prodigious episodic mass loss. The dust condensed from the atmosphere during the mass loss is responsible for the millimeter emission; the continuum emission seen at radio wavelengths probes the actual stellar photosphere. Molecules form along with the dust, and a steady state chemistry occurs in the dense inner regions (Tsuji 1973 A&A 23, 411). As the density of material drops, the chemistry freezes. But the molecules continue their long coast outward into the Galaxy, and as the shell thins ultraviolet light from the ambient galactic radiation field penetrates and initiates a new chemistry in the gas.
SiS, a simple molecule created in the dense inner envelope chemistry is photodissociated as it coasts out into the shell. The result is the centrally condensed emission we see in the present observations. Interestingly, in the next lower transition at 18 GHz, the line shape is much different from what we see here. At the extreme velocities in the profile, very bright narrow emission is seen which has been interpreted as maser emission. The interested student can find EVLA observations of this line in the archive.
HC3N, a much more complex species, is created by the photochemistry which becomes active as atoms and pieces of molecules destroyed by ultraviolet radiation undergo the next phase of chemistry in the shell. HC3N has many vibrational modes which may be excited in addition to its rotational modes. Owing to this, it can re-radiate energy absorbed from ultraviolet radiation more effectively than some molecules with a single bond. Eventually it too is destroyed however, but during its brief existence its rise to abundance in the envelope results in a ring of emission, which is what is observed in this image made with the EVLA. A recent model by Cordiner & Millar (2009, ApJ, 697, 68) describes a new chemical model for the shell, which also takes into account the variation of mass loss by the star. They show that in addition to purely chemical effects, local gas and dust density peaks play a role in shaping the observed emission.
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 files TAR in this directory.
# In UNIX wget 'ftp://ftp.aoc.nrao.edu/staff/gvanmoor/community_day/*'
How to Use This casaguide
There are a number of possible ways to run CASA. Many aspects are described in Getting_Started_in_CASA. You should review this page if you are new to CASA. In brief you can run CASA interactively by looking at the inputs to tasks with inp taskname (example: inp clean), setting the parameters one by one (example: selectdata=T) as you desire and then run go. After setting parameters one by one in a task and then looking at the inputs again, you will notice that the parameters that have been set to something other than their defaults are blue. If you have mistyped any parameters, they will be red and must be fixed for the task to run correctly. You can get more detailed help on any task by typing help taskname (example: help clean). Once a task is run you can get the same parameters back by running tget taskname (example: tget clean); subsequent runs will overwrite the previous tget file.
The second way to run CASA is to provide task function calls. This tutorial is made up of such calls, which were developed by looking at the inputs for each task and deciding what needed to be changed from default values. For task function calls, only parameters that you want to be different from their defaults need to be set. A series of task function calls can be combined together into a script, and run with execfile('scriptname.py'). It is possible to extract a script containing all the CASA task function calls in this and other casaguides using the method described at the Extracting_scripts_from_these_tutorials page.
If you are a relative novice or just new to CASA it is strongly recommended to work through this tutorial by cutting and pasting the task function calls provided below after you have read all the associated explanations. Work at your own pace, look at the inputs to the tasks to see what other options exist, and read the help files. Later, when you are more comfortable, you might try to extract the script, modify it for your purposes, and begin to reduce other data.
Initial Inspection and Flagging
listobs provides almost all relevant observational parameters such as correlator setup (frequencies, bandwidths, channel number and widths, polarization products), sources, scans, scan intents, and antenna locations:
# In CASA vis = 'day2_TDEM0003_20s_full' listobs(vis=vis)
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 +184.108.40.2064 J2000 2 32726 3 NONE IRC+10216 09:47:57.38200 +220.127.116.1100 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 +18.104.22.16889 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 -22.214.171.124 +126.96.36.199 1 ea02 E02 25.0 m -107.37.04.4 +33.54.01.1 2 ea03 E09 25.0 m -188.8.131.52 +184.108.40.206 3 ea04 W01 25.0 m -107.37.05.9 +33.54.00.5 4 ea05 W08 25.0 m -220.127.116.11 +18.104.22.168 5 ea07 N06 25.0 m -107.37.06.9 +22.214.171.124 6 ea08 N01 25.0 m -107.37.06.0 +33.54.01.8 7 ea09 E06 25.0 m -126.96.36.199 +188.8.131.52 8 ea12 E08 25.0 m -184.108.40.206 +220.127.116.11 9 ea15 W06 25.0 m -18.104.22.168 +22.214.171.124 10 ea19 W04 25.0 m -126.96.36.199 +188.8.131.52 11 ea20 N05 25.0 m -107.37.06.7 +33.54.08.0 12 ea21 E01 25.0 m -107.37.05.7 +184.108.40.206 13 ea22 N04 25.0 m -107.37.06.5 +33.54.06.1 14 ea23 E07 25.0 m -220.127.116.11 +18.104.22.168 15 ea24 W05 25.0 m -22.214.171.124 +126.96.36.199 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 +188.8.131.52
In addition to source names, antenna names, etc. CASA indexes each of these parameters staring from 0. When, say a field is specified, one can use the index or the name, both is accepted.
Note that the Rest Frequency and Systemic Velocity are wrong in the listobs log by a factor 10^6 and 1000, respectively, given the quoted units (MHz) and (km/s). This was due to a temporary error in the EVLA Observing Tool that has subsequently been fixed. Because the sky frequencies are correct, and we set the rest frequency explicitly later in the deconvolution stage, this does not present a problem for the data reductions.
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|
Look at a graphical plot of the antenna locations and save hardcopy in case you want it later. This will be useful for picking a reference antenna -- typically a good choice is an antenna close to the center of the array. Unless it shows problems after inspection of the data, we provisionally chose ea03.
# In CASA plotants(vis=vis,figfile='ant_locations.png')
Next, let's look at the elevation as a function of time for all sources. It's not the case for these data, but if the elevation is very low (usually at start or end of track) you may want to flag. Also, how near in elevation your flux calibrator is to your target will impact your ultimate absolute flux calibration accuracy. Unfortunately, the target and flux calibrator are not particularly well-matched for this observation, as you can show by plotting the elevation for each source (each sources has a different colors). We will be using data in spectral window 0, channels 4 to 60 for this plot as given by the spw='0:4~60' parameter, the general CASA selection syntax is described in the Measurement Selection Syntax Document:
# In CASA plotms(vis=vis, xaxis='time',yaxis='elevation',correlation='RR,LL', avgchannel='64',spw='0:4~60', coloraxis='field')
Thus we are strongly dependent on the opacity and gaincurve corrections to get the flux scale right for these data. (This is something to keep in mind when planning observations!)
Next, let's look at all the source amplitudes as a function of time.
# 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 is be shown in the logger. Since all the "located" baselines include ea12, this is the responsible antenna.
IMPORTANT NOTES ON PLOTMS: * When using the locate button it is important to have only selected a modest number of points with the mark region tool (see example of marked region in the thumnail), otherwise the response will be very slow and possibly hang the tool (all of the information will be output to your terminal window, not the logger). * Throughout the tutorial, when you are done marking/locate use the Clear Regions tool to get rid of the marked box before plotting other things. * After flagdata command flagging, you have to force a complete reload of the cache to look at the same plot again with the new flags applied. To do this change anything in the plotms GUI (the colorize parameter, antenna, spw, anything) and hit the plot button. * If the plotms tool does get hung during a plot try clicking the cancel button on the load progress GUI, and/or if you see a "table locked" message try typing clearstat on the CASA command line. * Occasionally plotms will get into a strange state that you cannot clear from inside. We are working on these issues, but for now, when all else fails, exit from casapy and restart.
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 set up a flagging command to get both bad antennas for the appropriate time and spw:
# 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.
Now remove the !ea07 from antenna and replot both spw, zooming in to be sure that all obviously low points are gone. Also zoom in and check 3C286 (J1229+0203 is already obvious because it is so bright!).
Lets look more closely at IRC+10216:
# In CASA plotms(vis=vis,field='3', xaxis='time',yaxis='amp',correlation='RR,LL', avgchannel='64',spw='0~1:4~60', coloraxis='spw')
You can see a that there are some noisy high points. But now try
# In CASA plotms(vis=vis,field='3', xaxis='uvdist',yaxis='amp',correlation='RR,LL', avgchannel='64',spw='0~1:4~60', coloraxis='spw')
Most of the high points on IRC+10216 are due to large scale emission on short baselines, but there is still some noisy stuff -- for a target like this with extended emission it's best to wait until later to decide what to do about it. We will not be able to get adequate calibration for antennas that are truly bad (even if they don't stand out here) so these will be obvious later.
Set Up the Model for the Flux Calibrator
Next, we set the model for the flux calibrator. Depending on your observing frequency and angular resolution you can do this several ways. In the past, one typically used a point source (constant flux) model for the flux calibrator, possibly with a uvrange cutoff if necessary. More recently for the VLA/EVLA, model images for the most common flux calibrators have been made available for use in cases where the sources are somewhat resolved. This is most likely to be true at higher frequencies and at higher resolutions (more extended arrays).
Currently, CASA contains models for the most common calibrators at the most common frequencies but not yet the new EVLA frequency bands S, Ku, and Ka. These will be added as son as they become available. One may check the availability of calibration models in setjy:
# In CASA setjy(vis=vis,listmodimages=T)
The terminal will now show the available models, e.g. 3C286_C.im, 3C48_K.im etc. (setjy will search in the working directory for images that may contain models, as well as in a CASA directory where known calibrator models are stored)
As mentioned above, the Ka band does not yet have a model. We will use the K-band model instead.
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 -- it is a good idea to save this information for your records.
# In CASA setjy(vis=vis,field='7',spw='0~1', modimage='3C286_K.im')
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.
Before determining the bandpass solution, we need to inspect phase and amplitude variations with time and frequency on the bandpass calibrator to decide how best to proceed. We limit the number of antennas to make the plot easier to see. We chose ea02 as it seems like a good candidate for the reference antenna.
# In CASA plotms(vis=vis,field='5', xaxis='channel',yaxis='phase',correlation='RR', avgtime='1e8',spw='0:4~60',antenna='ea02&ea23')
The phase variation is modest ~10 degrees. Now expand to all baselines that include ea02, then hit the "Plot" button.
# In CASA plotms(vis=vis,field='5', xaxis='channel',yaxis='phase',correlation='RR', avgtime='1e8',spw='0:4~60',antenna='ea02', coloraxis='antenna2')
From this you can see that the phase variation across the bandpass is modest. Next check LL, and spw=1, both correlations. Also check other antennas if you like.
Now look at the phase as a function of time.
# In CASA plotms(vis=vis,field='5', xaxis='time',yaxis='phase',correlation='RR', avgchannel='64',spw='0:4~60',antenna='ea02&ea23')
Expand to all antennas with ea02
# In CASA plotms(vis=vis,field='5', xaxis='time',yaxis='phase',correlation='RR', avgchannel='64',spw='0:4~60',antenna='ea02', coloraxis='antenna2')
You may want to select "Custom" under "unflagged points symbol" and then change Style from "2" to "3" under the "Display" tab, "Unflagged Points Symbol" .
You can see that the phase variations are smooth, but do vary significantly over the 5 minutes of observation -- in most cases by a few 10s of degrees. Zoom in to see this better if you want.
The conclusion from this investigation is that you need to correct the phase variations with time before solving for the bandpass to prevent decorrelation of the vector averaged bandpass solution. Since the phase variation as a function of channel is modest, you can average over several channels to increase the signal to noise of the phase vs. time solution. If the phase variation as a function of channel is larger you may need to use only a few channels to prevent introducing delay-based closure errors as can happen from averaging over non-bandpass corrected channels with large phase variations.
Since the bandpass calibrator is quite strong we do the phase-only solution on the integration time of 20 seconds (solint='int').
# In CASA gaincal(vis=vis,caltable='bpphase.gcal', field='5',spw='0~1:20~40', refant='ea02',calmode='p',solint='int',minsnr=2.0, gaincurve=T)
Plot the solutions
# In CASA plotcal(caltable='bpphase.gcal',xaxis='time',yaxis='phase', iteration='antenna',subplot=331,plotrange=[0,0,-180,180])
These solutions will appear in the CASA plotter gui. If you closed it after plotting the antennas above, it should reopen. If it is still open from before, the new plots should just appear. After you are done looking at the first set of plots, push the "Next" button on the GUI to see the next set of antennas.
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)
A few words about solint and combine:
The use of solint='inf' in bandpass will derive one bandpass solution for the whole J1229+0203 scan. Note that if there had been two observations of the bandpass calibrator (for example), this command would have combined the data from both scans to form one bandpass solution, because the default of the combine parameter for bandpass is combine='scan'. To solve for one bandpass for each bandpass calibrator scan you would also need to include combine=' ' in the bandpass call. In all calibration tasks, regardless of solint, scan boundaries are only crossed when combine='scan'. Likewise, field (spw) boundaries are only crossed if combine='field' (combine='spw'), the latter two are not generally good ideas for bandpass solutions.
Plot the solutions, amplitude and phase:
# 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)
Note that phases on ea12 look noiser than on other antennas. This jitter could indicate bad pointing; note that ea12 had just come back in the array.
This step isn't necessary from a calibration perspective, but if you want to go ahead and check the bandpass calibration on the bandpass calibrator you can run applycal here. In future we hope to plot corrected data on-the-fly without this applycal step. Later applycals will overwrite this one, so no need to worry.
applycal(vis=vis,field='5', gaintable='bandpass.bcal', gainfield=['','5'], gaincurve=T,calwt=F)
Similar to flagdata, applycal works like a matrix. The first entries in the lists are to be used together, so are the second entries etc. All will be applied to the 'field' selection. In the above example, the 'bandpass.cal' that was obtained for field '5' (the bandpass observation) is also applied to field '2'. Again, lists within the lists are fine.
plotms(vis=vis,field='5', xaxis='channel',yaxis='phase',ydatacolumn='corrected', correlation='RR', avgtime='1e8',spw='0:4~60',antenna='ea02', coloraxis='antenna2')
plotms(vis=vis,field='5', xaxis='channel',yaxis='amp',ydatacolumn='corrected', correlation='RR', avgtime='1e8',spw='0:4~60',antenna='ea02', coloraxis='antenna2')
Note that the phase and amplitude as a function of channel are very flat now.
Now that we have a bandpass solution to apply we can solve for the antenna-based phase and amplitude gain calibration. Since the phase changes on a much shorter timescale than the amplitude, we will solve for them separately. In particular, if the phase changes significantly over a scan time, the amplitude would be decorrelated, if the un-corrected phase were averaged over this timescale. Note that we re-solve for the gain solutions of the bandpass calibrator, so we can derive new solutions that are corrected for the bandpass shape. Since the bandpass calibrator will not be used again, this is not strictly necessary, but is useful to check its calibrated flux density for example. We use a minimum signal-to-noise of 2 here as it seems to be a good compromise for using good data without rejecting too many solutions (minsnr=2).
# In CASA gaincal(vis=vis,caltable='intphase.gcal', field='2,5,7',spw='0~1:4~60', refant='ea02',calmode='p',solint='int',minsnr=2.0, gaintable='bandpass.bcal', gaincurve=T)
Here solint='int' coupled with calmode='p' will derive a single phase solution for each 10 second integration. Note that the bandpass table is applied on-the-fly before solving for the phase solutions, however the bandpass is NOT applied to the data permanently until applycal is run later on.
Note that quite a few solutions are rejected due to SNR<2 (printed to terminal). For the most part it is only one or two solutions out of >30 so this isn't too worrying. Take note if you see large numbers of rejected solutions per integration. This is likely an indication that solint is too short for the S/N of the data.
Now look at the phase solution, and note the obvious scatter within a scan time.
# In CASA plotcal(caltable='intphase.gcal',xaxis='time',yaxis='phase', iteration='antenna',subplot=331,plotrange=[0,0,-180,180])
Although solint='int' (i.e. the integration time of 20 seconds) is the best choice to apply before for solving for the amplitude solutions, it is not a good idea to use this to apply to the target. This is because the phase-scatter within a scan can dominate the interpolation between calibrator scans. Instead, we also solve for the phase on the scan time, solint='inf' (but combine=' ', since we want one solution per scan) for application to the target later on. Unlike the bandpass task, for gaincal, the default of the combine parameter is combine=' '.
# In CASA gaincal(vis=vis,caltable='scanphase.gcal', field='2,5,7',spw='0~1:4~60', refant='ea02',calmode='p',solint='inf',minsnr=2.0, gaintable='bandpass.bcal', gaincurve=T)
# In CASA plotcal(caltable='scanphase.gcal',xaxis='time',yaxis='phase', iteration='antenna',subplot=331,plotrange=[0,0,-180,180])
Note that there are no failed solutions here because of the added S/N afforded by the longer solint. Alternatively, instead of making a separate phase solution for application to the target, one can also run smoothcal to smooth the solutions derived on the integration time.
Next we apply the bandpass and solint='int' phase-only calibration solutions on-the-fly to derive amplitude solutions. Here the use of solint='inf', but combine=' ' will result in one solution per scan interval.
# In CASA gaincal(vis=vis,caltable='amp.gcal', field='2,5,7',spw='0~1:4~60', refant='ea02',calmode='ap',solint='inf',minsnr=2.0, gaintable=['bandpass.bcal','intphase.gcal'], gaincurve=T)
Now let's look at the resulting phase solutions. Since we have taken out the phase as best we can by applying the solint='int' phase-only solution, this plot will give a good idea of the residual phase error. If you see scatter of more than a few degrees here, you should consider going back and looking for more data to flag, particularly bad timeranges etc.
# In CASA plotcal(caltable='amp.gcal',xaxis='time',yaxis='phase', iteration='antenna',subplot=331)
Indeed, both antenna ea12 (all times) and ea23 (first 1/3 of observation) show particularly large residual phase noise.
# In CASA plotcal(caltable='amp.gcal',xaxis='time',yaxis='amp', iteration='antenna',subplot=331)
Note that the amplitude solutions for ea12 are very low; this is another indication that this antenna is dubious.
Next we use the flux calibrator (whose flux density was set in setjy above) to derive the flux of the other calibrators. Note that the flux table REPLACES the amp.gcal in terms of future application of the calibration to the data, i.e. the flux table contains both the amp.gcal and flux scaling. Unlike the gain calibration steps, this is not an incremental table.
# In CASA fluxscale(vis=vis,caltable='amp.gcal', fluxtable='flux.cal',reference='7')
It is a good idea to note down for your records the derived flux densities:
Flux density for J0954+1743 in SpW=0 is: 0.237135 +/- 0.000858511 (SNR = 276.216, nAnt= 19) Flux density for J0954+1743 in SpW=1 is: 0.247597 +/- 0.00063498 (SNR = 389.928, nAnt= 19) Flux density for J1229+0203 in SpW=0 is: 26.508 +/- 0 (SNR = inf, nAnt= 19) Flux density for J1229+0203 in SpW=1 is: 26.2517 +/- 0 (SNR = inf, nAnt= 19)
Obviously, the signal-to-noise for J1229+0203 can't be infinity! This is just an indication that there is only one scan for this source, and we derived a scan based amplitude solution, so there is no variation to calculate.
Next, check that the flux.cal table looks as expected.
# In CASA plotcal(caltable='flux.cal',xaxis='time',yaxis='amp', iteration='antenna',subplot=331)
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.
# 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)
# 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)
# 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)
For the target we apply the bandpass from id=5, and the calibration from the gain calibrator (id=2):
# 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)
Now inspect the corrected data:
# 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')
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.
# 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')
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.
From the locate we also find that ea12 and ea23 show some high points; to see this, replot baselines using each of them alone:
plotms(vis=vis,field='2',ydatacolumn='corrected', xaxis='time',yaxis='amp',correlation='RR,LL', avgchannel='64',spw='0:4~60',antenna='ea12', coloraxis='antenna2')
plotms(vis=vis,field='2',ydatacolumn='corrected', xaxis='time',yaxis='amp',correlation='RR,LL', avgchannel='64',spw='0:4~60',antenna='ea23', coloraxis='antenna2')
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.
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.
# 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')
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
# 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')
To see if it's restricted to a certain time
# 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')
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.
# In CASA flagdata(vis=vis, field=['',''], spw=['',''], antenna=['ea07,ea12,ea28','ea07,ea23'], timerange=['','03:21:40~04:10:00'])
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.
# 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)
# In CASA bandpass(vis=vis,caltable='bandpass_redo.bcal', field='5', refant='ea02',solint='inf',solnorm=T, gaintable=['bpphase_redo.gcal'], gaincurve=T)
# 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)
# 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)
# 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)
# In CASA fluxscale(vis=vis,caltable='amp_redo.gcal', fluxtable='flux_redo.cal',reference='7')
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)
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.
# 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)
# 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)
# 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)
# 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)
Now you can inspect the calibrated data again. Except for random scatter things look pretty good.
# 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')
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.
# 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')
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:
# In CASA split(vis=vis,outputvis='J0954', field='2')
# In CASA split(vis=vis,outputvis='IRC10216', field='3')
To reinitialize the scratch columns for use by later tasks, we need to run clearcal for both new datasets
# In CASA clearcal(vis='J0954')
# In CASA clearcal(vis='IRC10216')
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 part2.
Last checked on CASA Version 3.3.0.