EVLA Spectral Line Imaging Analysis IRC+10216
This tutorial is under construction. There are several things still to be added For the time being (until release candidate is built) you want to run this on casapy-test
This tutorial picks up where EVLA Spectral Line Calibration IRC+10216 leaves off.
UV Continuum Subtraction and Setting Up for Self-Calibration
Now we can make a vector averaged uv-plot of the calibrated target spectral line data. It is important to note that you will only see signal in such a plot if (1) the data are well calibrated, and (2) there is significant signal near the phase center of the observations, or if the line emission (or absorption) is weak but extended. If this isn't true for your data, you won't be able to see the line signal in such a plot and will need to make an initial (dirty or lightly cleaned) line+continuum cube to determine the line-free channels. Generally, this is the recommended course for finding the line-free channels more precisely than is being done here due to time constraints, as weak line signal would not be obvious in this plot.
plotms(vis='IRC10216',field='',ydatacolumn='corrected',
xaxis='channel',yaxis='amp',correlation='RR',
avgtime='1e8',avgscan=T,spw='0~1:4~60',antenna='')
in the Display tab, choose colorize by spw and change the Unflagged points symbol to custom and Style of 3.
You should see the "horned profile" typical of a rotation shell. From this plot, you can guess that strong line emission is restricted to channels 18 to 47 (zoom in if necessary to see exactly what the channel numbers are).
In the Data tab you can also click on "all baselines" to average all baselines, but this is a little harder to see.
Now we want to use the line free channels to create a model of the continuum emission that can be subtracted to form a line-only dataset. We want to refrain from going to close to the edges of the band -- these channels are typically noisy, and we don't want to get too close to the line channels because we could only see strong line emission in the vector averaged uv-plot.
uvcontsub2(vis='IRC10216',fitspw='0~1:4~13;52~60',
want_cont=T)
The "want_cont=T" will produce two new datasets, IRC10216.contsub is the continuum subtracted line data, and IRC10216.cont is the continuum estimate (note however, that it is still a multi-channel dataset).
If you want to try self-cal: Unfortunately, at the moment, uvcontsub2 doesn't leave the continuum subtracted line dataset in the state you need *if* you think you might want to self-calibrate the data later. This is because clean always looks at the "corrected" datacolumn, while gaincal (also used for self-calibration) always looks at the "data" column. You also need to know that unless the imagename supplied to clean already exists, clean always overwrites the model column with the clean model (this will be the model supplied to the self-calibration process).
The files produced by uvcontsub2 will have the following in their data columns:
Data Model Corrected
IRC10216.contsub Line+Cont Cont Line
IRC10216.cont Cont Cont Cont
Now the imaging task clean will clean both of these files fine, and correctly overwrite the model data column with the correct clean model. However, if you try to self-cal (i.e. run gaincal) on the continuum subtracted line data (.contsub), it will use the Line+Cont as its input along with the line only clean model. If the line and continuum have significantly different morphology (almost always) the self-cal process will fail. In contrast the continuum dataset (.cont) will work fine because the Data and Corrected columns agree.
To fix things up, we must split the "corrected" column (which places "Corrected" in the "Data" column of the new dataset. Then you can either run clearcal to reinitialize the "model" and "corrected" columns or let clean do it for you. We put this step here explicitly for clarity about the process.
split(vis='IRC10216.contsub',outputvis='IRC10216.contsub.data')
clearcal(vis='IRC10216.contsub.data')
Now IRC10216.contsub.data will have the Line in the "data" column, 1 in the "model" column, and the Line in the "corrected" column as desired.
We expect that these extra shenanigans will be unnecessary in the future.
Image the Spectral Line Data
Here we make images from the continuum-subtracted, calibrated spectral line data. Because the spectral line emission from IRC+10216 has significant extended emission, it is very important to run clean interactively, and make a clean mask. To make the cube a bit smaller and stay away from noisy edge channels we restrict the channel range using the spw parameter.
# In CASA
clean(vis='IRC10216.contsub.data',imagename='IRC10216_HC3N.cube_r0.5',
imagermode='csclean',
imsize=300,cell=['0.4arcsec'],spw='0:5~58',
mode='velocity',interpolation='linear',
restfreq='36.39232GHz',outframe='LSRK',
weighting='briggs',robust=0.5,
interactive=T,
threshold='3.0mJy',niter=100000)
It will take a little while to grid the data, but the viewer will open when it's ready to start an interactive clean. Use the "tape deck" at the bottom of the Viewer display GUI to step through to the channel with the most extended (in angular size) emission, select "all channels" for the clean mask, select the polygon tool (second in from the right) and make a single mask that applies to all channels (see example in thumbnail). Once you make the polygon region, you need to double click inside it to save the mask region -- if you see the polygon turn white you will know you succeeded. Note, that if you had the time and patience you could make a clean mask for each channel, and this would create a slightly better result.
After making the mask you should check that the emission in all the other channels fits within the mask you made using the "tape deck" to move back and forth. If you need to include more area in the mask, you can chose the "erase" toggle at the top, and then encircle your existing mask with a polygon and double click inside. Then go back to "add" toggle at top and make a new mask. Alternatively, you can erase a part of the mask, or you can add to the existing mask by drawing new polygons. Feel free to experiment with this a bit.
note: If you start an interactive clean, and then do not make a mask, clean will stop when you tell it to go on because it has nothing to clean. There is no default mask.
To continue with clean use the "Next action" buttons in the green area on the Viewer Display GUI: The red X will stop clean where you are, the blue arrow will stop the interactive part of clean, but continue to clean non-interactively until reaching the stopping niter or threshold (whichever comes first), and the green arrow will clean until it reaches the "iterations" parameter on the left side of the green area. When the interactive viewer comes back use the tape deck to recheck that you mask encompasses what you think is real emission. The middle mouse button by default controls the image stretch.
Note that for this example, threshold has been set to threshold='3mJy' to protect you from cleaning too deeply. With a careful clean mask you can clean to close to the thermal noise limit (note here I mean the actual observed rms noise limit and not the theoretical one you calculated for the proposal, as flagging, weather etc can affect what you actually get). It is ALWAYS best to clean each channel in a cube to a specific threshold than to stop using the niter parameter, which can leave each channel cleaned to different levels. There are many ways to determine a suitable threshold. One way is to make a dirty image (niter=0), open the cube using the viewer, go to a line free channel, select the box region tool, make a box near the field center about the size of your source, double click inside. The rms noise of that channel will appear in a pop-up window (rms noise for whole cube will go to terminal). Try a few different boxes, average the results and this is a good estimate of the rms per channel assuming your data are not dynamic range limited (i.e. noise can be higher in channels with strong signal). This is the absolute minimum for threshold. With no mask you probably shouldn't clean deeper than 3x this rms.
Keep cleaning, by using the green Next Action arrow until the residual displayed in the viewer looks "noise like". To speed things up, you might change the iteration parameter in the viewer to something like 300. This parameter can also be set in the task command. You will notice that in this particular case, there are residuals that cannot be cleaned -- these are due to the extended resolved out structure on size scales larger than the array is sensitive to (the "Largest Angular Scale" or LAS that the array is sensitive to can be calculated from the shortest baseline length), and potential residual phase and amplitude calibration errors. We will explore this in a few sections with self-calibration.
Repeat the process for the SiS line using the call below, note that the emission for this line is less extended than the HC3N -- this has to do with the different excitation requirements of the two different lines. The SiS is excited closer to the central star than the HC3N.
# In CASA
clean(vis='IRC10216.contsub.data',imagename='IRC10216_SiS.cube_r0.5',
imagermode='csclean',
imsize=300,cell=['0.4arcsec'],spw='1:5~58',
mode='velocity',interpolation='linear',
restfreq='36.30963GHz',outframe='LSRK',
weighting='briggs',robust=0.5,
interactive=T,
threshold='3.0mJy',niter=100000)
Image the Continuum data
Below the use of mode='mfs' will make a single multi-frequency synthesis image out of the specified spw. Again you should make an interactive clean mask. Since no threshold is set, you will need to stop cleaning when the residual looks noise like using the red x "Next Action" button (it will be done when the viewer comes back the second time).
# In CASA
clean(vis='IRC10216.cont',imagename='IRC10216.36GHzcont',
mode='mfs',imagermode='csclean',
imsize=300,cell=['0.4arcsec'],spw='0~1:5~59',
weighting='briggs',robust=0.5,
interactive=T)
Now look at the result in the viewer if you like
# In CASA
Viewer
Image Analysis and Viewing
Next make integrated intensity maps (moment 0) and integrated velocity maps (moment 1). To do this, we'll want to know what channels the line emission starts and ends on, and also the rms noise in a single channel. So first lets open the viewer:
# In CASA
viewer
Then use the Viewer tape deck to see which channels have significant line emission. For HC3N, the line channel range in the cube is 11 to 40, and it is the same for SiS.
Then use the tape deck to go to a line free channel, select the box region tool and make a box. When you double click in the box, the image statistics for the whole cube will print to the terminal and for the channel you are on, it will print to a pop up window. Move the box around a bit to see what the variation in rms noise is. You should get something like 2 mJy. Note that the rms is much worse in channels with strong emission because of the low dynamic range of these data. If you want the box tool to go away (i.e. if you want to make a new one), hit the escape key.
Now lets make the moment 0 and moment 1 maps. For moment zero, its best to limit the calculation to image channels with significant signal in them, but not to apply a flux cutoff, as this will bias the derived integrated intensities upward.
# In CASA
immoments(imagename='IRC10216_HC3N.cube_r0.5.image',moments=[0],
axis='spectral',
chans='11~40',
outfile='IRC10216_HC3N.cube_r0.5.image.mom0')
# In CASA
immoments(imagename='IRC10216_SiS.cube_r0.5.image',moments=[0],
axis='spectral',
chans='11~40',
outfile='IRC10216_SiS.cube_r0.5.image.mom0')
For moment 1, it is essential to apply a conservative flux cutoff to limit the calculation to high signal-to-noise areas. Here we use about 5sigma.
# In CASA
immoments(imagename='IRC10216_HC3N.cube_r0.5.image',moments=[1],
axis='spectral',
chans='11~40',excludepix=[-100,0.01],
outfile='IRC10216_HC3N.cube_r0.5.image.mom1')
# In CASA
immoments(imagename='IRC10216_SiS.cube_r0.5.image',moments=[1],
axis='spectral',
chans='11~40',excludepix=[-100,0.01],
outfile='IRC10216_SiS.cube_r0.5.image.mom1')
Now user the viewer to further explore the images you've made.
Self-Calibration
The many different aspects of self-calibration could fill several casaguides. Here we describe a simple process for this particular relatively low S/N data (low S/N per channel at least).
While running clean above, the model column for each channel will have been filled with the clean model (if you made a Fourier transform of this model, you would see an image of the clean components).
We chose to do the self cal on the spw=1 SiS line data because it has the strongest emission in a single channel and is a bit more compact than the HC3N data. We will run gaincal specifying the channel in the uv-data that has the brightest peak in the image (use the viewer to figure out which channel this is for spw=1). Since we started the image with a channel range we need to account for the fact that the image channel numbers do not map exactly to the uv-data channel numbers (they are off by 5 so that channel 13 in the image is roughly channel 19 in the uv-data).
The next thing we need to understand is the S/N of the data. In particular, to self-cal, you need enough signal on a single baseline over the course of your chosen solint to get a S/N of about 3. Above we calculated an average rms noise of about 2 mJy/beam/channel for the whole timerange (about 95 minutes on source time) and all antennas (16). We can use our knowledge of the radiometer equation where rms scales as 1/sqrt(time*#baselines), and the number of baselines= N(N-1)/2 and N=# of antennas to find the rms noise on one baseline, one 10 second integration:
2 mJy/beam/channel *[math]\displaystyle{ \sqrt{[(95*60/10)*(16*15)/(2*1)]} }[/math]
The 95 minutes of on-source time can be estimate from a plot like
plotms(vis='day2_TDEM0003_10s_norx',field='3',ydatacolumn='corrected',
xaxis='time',yaxis='amp',correlation='RR,LL',
avgchannel='64',spw='0:4~60',antenna='')
This analysis suggests that the rms noise on one baseline, one 10 second integration is about 525 mJy. In contrast the peak flux density in the strongest SiS channel is only about 200 mJy (you can check using the viewer). This tells us that we need to use a solint large enough so that the rms noise is not worse than about 1/3 of 200 mJy. Thus, a solint of 10 minutes is about the shortest we can use and be confident of the solutions.
Now we run gaincal with the solint we have determined. Note that because our desired solint is more than the scan time, we need to include combine='scan'.
# In CASA
gaincal(vis='IRC10216.contsub.data',caltable='pcal_ch19one_10min',
spw='1:19~19',calmode='p',solint='10min',combine='scan',
refant='ea02',minsnr=3.0)
Now lets look at the solutions:
# In CASA
plotcal(caltable='pcal_ch19one_10min',xaxis='time',yaxis='phase',
iteration='antenna',subplot=331,plotrange=[0,0,-50,50])
For some antennas you can see clear global trends away from zero: ea08, ea21,ea24 are examples, and you can also see some smaller variations with time.
Now lets explore whether applying this solution actually improves matters. To do this we need to run applycal to apply the solutions to the line dataset, both spw. We need to use spwmap to tell it that the solutions derived for spw=1 should be applied to both spw=0 and spw=1. Again its important to set calwt=F here.
# In CASA
applycal(vis='IRC10216.contsub.data',field='',spw='0,1',
gaintable=['pcal_ch19one_all'],spwmap=[[1,1]],calwt=F)
Note: in this example we ran the self-cal steps on the full uv continuum subtracted spectral line data set. For a more complex iterative self-calibration proceedure, you may find it easier to split off the channel/spw you want to experiment on with split, and then do all the imaging (clean) and gaincal steps with it. The gaincal tables created on the single channel can still be applied with applycal to the multi-channel/spw dataset. If you do this though, keep in mind that once split, the single-channel data will have its spw id reset to 0 (you can check with listobs), no matter what spw it came from. Thus in order to applycal with it you would need spwmap=[[0,0]].
To save time we can use the clean mask we made before and run in a non-interactive mode. You can use a mask over again as long as the number channels in the clean call haven't changed. You can change cell or imsize and it will still do the right thing.
clean(vis='IRC10216.contsub.data',imagename='IRC10216_HC3N.cube_r0.5.selfcal',
imagermode='csclean',
imsize=300,cell=['0.4arcsec'],spw='0:5~58',
mode='velocity',interpolation='linear',
restfreq='36.39232GHz',outframe='LSRK',
weighting='briggs',robust=0.5,
mask='IRC10216_HC3N.cube_r0.5.mask',
interactive=F,threshold='3.0mJy',niter=100000)
# In CASA
clean(vis='IRC10216.contsub.data',imagename='IRC10216_SiS.cube_r0.5.selfcal',
imagermode='csclean',calready=T,
imsize=300,cell=['0.4arcsec'],spw='1:5~58',
mode='velocity',interpolation='linear',
restfreq='36.30963GHz',outframe='LSRK',
weighting='briggs',robust=0.5,
mask='IRC10216_SiS.cube_r0.5.mask',
interactive=F,threshold='3.0mJy',niter=100000)
Now investigate the original and self-cal'ed images in the viewer. You will find that even this single self-cal step that averaged over the whole timerange significantly improves the images.
