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| This script steps you through continuum imaging and self calibration
| | #REDIRECT [[First_Look_at_Self_Calibration_CASA_6.5.4]] |
| of the science data for our science target, TW Hydra.
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| You should have downloaded the data package as part of the previous imaging tutorial. If you haven't done that yet, check the [http://casaguides.nrao.edu/index.php?title=First_Look_at_Imaging First Look At Imaging] Guide for instructions.
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| In that first tutorial you made a first continuum image in the previous imaging lesson. We start here by repeating that step and then we iteratively self-calibrate the
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| data, focusing on short-timescale phase corrections.
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| First, copy the calibrated and flagged data from the working directory. Remember that this is our best version of the data.
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| <source lang="python">
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| # In CASA
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| os.system("rm -rf sis14_twhya_calibrated_flagged.ms")
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| os.system("cp -r ../working_data/sis14_twhya_calibrated_flagged.ms .")
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| </source>
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| Run a quick listobs to get oriented:
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| <source lang="python">
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| # In CASA
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| listobs("sis14_twhya_calibrated_flagged.ms")
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| </source>
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| Now, use tclean to make a continuum image of TW Hydra (field 5). This call is interactive, but the automated approach that we used
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| in the last lesson would also work. See the last lesson for details. Clean until the residuals near TW Hydra are comparable
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| to those in the rest of the image. For example, you might place your clean mask over the central feature, only, and clean with for two main cycles.
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| That is, use the green arrow twice, and then click the red X to finish tclean.
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| <figure id="Imaging-tutorial-selfcal-1.png">
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| [[File:Imaging-tutorial-selfcal-1.png|thumb|<caption>Residuals from the first tclean after 2 main cycles of cleaning.</caption>]]
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| </figure>
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| <source lang="python">
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| # In CASA | |
| os.system('rm -rf first_image.*')
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| tclean(vis='sis14_twhya_calibrated_flagged.ms',
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| imagename='first_image',
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| field='5',
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| spw='',
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| specmode='mfs',
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| deconvolver='hogbom',
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| nterms=1,
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| gridder='standard',
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| imsize=[250,250],
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| cell=['0.08arcsec'],
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| weighting='natural',
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| threshold='0mJy',
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| niter=5000,
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| interactive=True,
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| savemodel='modelcolumn')
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| </source>
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| In addition to creating an image, TCLEAN saves the cleaned "model" of the science target with
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| the measurement set if the parameter savemodel="modelcolumn". This model is required for later self-calibration steps. Note, in the
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| previous lessons we only had models for the calibrators, not the science target itself. Of
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| course this model for our science target is not perfect, only as good as the first clean, but it's a
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| good starting point.
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| In CASA 5.1, you will may see a warning requesting you check in the CASA log that the model was created. Look for the line in the log that says 'Saving model column':
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|
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| <pre style="background-color: #fffacd;">
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| INFO task_tclean Saving model column
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| INFO task_tclean [first_image] Peak residual (max,min) within mask : (0.0293835,-0.0138908) over full image : (0.0514144,-0.0328941)
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| INFO task_tclean [first_image] Total Model Flux : 1.13002
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| INFO tclean Reached global stopping criterion : force stop
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| INFO SDAlgorithmBase [first_image] : Restoring model image.
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| WARN tclean Please check the casa log file for a message confirming that the model was saved after the last major cycle. If it doesn't exist, please re-run tclean with niter=0,calcres=False,calcpsf=False in order to trigger a 'predict model' step that obeys the savemodel parameter.
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| </pre>
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| With a model in place, we are in a position to calibrate the science
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| target directly. We use {{gaincal}}, which is the task used both for general gain calibration using an external calibrator, and for self-calibration. We will focus here on phase corrections - generally good practice
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| for self calibration - because amplitude self calibration has a
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| larger potential to change the source characteristics (i.e. introduce artifacts).
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| Figuring out the best averaging parameters is often the key to good
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| self-calibration. You would like the solution interval to be short enough so that it tracks changes in the atmospheric phase with high accuracy, but long enough so that you measure phases with good signal-to-noise.
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| Also, ideally you'd like to keep solutions separate for difference spw's and polarizations, but for faint sources when you need to boost SNR, it may be necessary to average over these parameters to achieve good
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| solutions. Using 30 seconds for the solution interval is a good choice for TW Hydra.
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| <source lang="python">
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| # In CASA
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| os.system("rm -rf phase.cal")
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| gaincal(vis="sis14_twhya_calibrated_flagged.ms",
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| caltable="phase.cal",
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| field="5",
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| solint="30s",
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| calmode="p",
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| refant="DV22",
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| gaintype="G")
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| </source>
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| Try playing around with different solution intervals or averaging
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| options. Bear in mind that you want the shortest possible interval
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| while also retaining separate SPW and polarizations. However, none
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| of this helps you if you don't get good solutions. So you generally
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| will experiment with the following options: (1) combine="scan" or "spw" to
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| allow solutions to cross SPW/scan boundaries, or you can do both using combine="scan,spw"; (2) increase solint to set the
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| solution interval; and (3) toggling gaintype between "G" and "T" (the former generates solutions independently for each polarization, and the latter averages two polarizations before determining the solutions).
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| Plot the resulting solutions. We are finding nontrivial, though not
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| enormous, solutions (a few 10s of degrees) with the two correlations
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| tracking one another pretty well. If the data were already perfectly
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| calibrated, these values would solve to be zero.
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| <figure id="Imaging-tutorial-selfcal-plotcal-1.png">
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| [[File:Imaging-tutorial-selfcal-plotcal-1.png|thumb|<caption>Phase solutions after the first round of self calibration.</caption>]]
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| </figure>
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| <source lang="python">
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| # In CASA
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| plotcal(caltable="phase.cal",
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| xaxis="time",
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| yaxis="phase",
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| subplot=331,
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| iteration="antenna",
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| plotrange=[0,0,-30,30],
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| markersize=5,
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| fontsize=10.0,
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| figfile="sis14_selfcal_phase_scan.png",
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| showgui = True)
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| </source>
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| We are happy with this solution. So let's apply it to the data using
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| {{applycal}}. We only care about field 5 (the science target).
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| <source lang="python">
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| # In CASA
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| applycal(vis="sis14_twhya_calibrated_flagged.ms",
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| field="5",
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| gaintable=["phase.cal"],
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| interp="linear")
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| </source>
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| At this point the self-calibrated data are stored in the MS in the "corrected data"
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| column. Because we will want to try more rounds of self calibration,
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| it's often useful (though not strictly necessary) at this point to
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| split out the corrected data into a new data set. '''Note if restarting this tutorial, need to also delete .flagversions file'''
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| <source lang="python">
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| # In CASA
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| os.system("rm -rf sis14_twhya_selfcal.ms sis14_twhya_selfcal.ms.flagversions")
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| split(vis="sis14_twhya_calibrated_flagged.ms",
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| outputvis="sis14_twhya_selfcal.ms",
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| datacolumn="corrected")
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| </source>
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| Now clean the self-calibrated data. Again, clean until the residuals
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| on TW Hydra resemble those in the surrounding image.
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| <figure id="Imaging-tutorial-selfcal-2.png">
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| [[File:Imaging-tutorial-selfcal-2.png|thumb|<caption>Residuals from the second tclean after 2 main cycles of cleaning. </caption>]]
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| </figure>
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| <source lang="python">
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| # In CASA
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| os.system('rm -rf second_image.*')
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| tclean(vis='sis14_twhya_selfcal.ms',
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| imagename='second_image',
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| field='5',
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| spw='',
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| specmode='mfs',
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| deconvolver='hogbom',
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| nterms=1,
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| gridder='standard',
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| imsize=[250,250],
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| cell=['0.1arcsec'],
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| weighting='natural',
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| threshold='0mJy',
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| interactive=True,
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| niter=5000,
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| savemodel='modelcolumn')
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| </source>
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| The residuals do look better this time around. Run the viewer and
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| compare the first and second images. You should see a noticeable
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| improvement in the noise and some improvement in the signal, so that
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| the overall signal-to-noise (dynamic range) is much improved.
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| This second clean also produces a model (if the savemodel parameter is set!), hopefully a mildly better one this time.
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| Now we will run a second round of phase-only self calibration using the improved model.
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| <source lang="python">
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| # In CASA
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| os.system("rm -rf phase_2.cal")
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| gaincal(vis="sis14_twhya_selfcal.ms",
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| caltable="phase_2.cal",
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| field="5",
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| solint="30s",
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| calmode="p",
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| refant="DV22",
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| gaintype="G")
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| </source>
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| Let's plot the calibration table again. At this point, we see much smaller phase scatter
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| relative to the model, so we don't expect more phase-only self calibration to
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| do much.
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| <figure id="Imaging-tutorial-selfcal-plotcal-2.png">
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| [[File:Imaging-tutorial-selfcal-plotcal-2.png|thumb|<caption>Phase solutions after the second round of self calibration.</caption>]]
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| </figure>
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| <source lang="python">
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| # In CASA
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| plotcal(caltable="phase_2.cal",
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| xaxis="time",
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| yaxis="phase",
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| subplot=331,
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| iteration="antenna",
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| plotrange=[0,0,-30,30],
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| markersize=5,
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| fontsize=10.0,
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| figfile="sis14_selfcal_phase_scan_2.png",
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| showgui = True)
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| </source>
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| Apply the solutions again:
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| <source lang="python">
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| # In CASA
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| applycal(vis="sis14_twhya_selfcal.ms",
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| field="5",
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| gaintable=["phase_2.cal"],
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| interp="linear")
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| </source>
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| Split the data off again. Here you can see the work flow for heavily iterative
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| self-calibration. We progressively calibrate, split.
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| <source lang="python">
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| # In CASA
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| os.system("rm -rf sis14_twhya_selfcal_2.ms sis14_twhya_selfcal_2.ms.flagversions")
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| split(vis="sis14_twhya_selfcal.ms",
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| outputvis="sis14_twhya_selfcal_2.ms",
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| datacolumn="corrected")
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| </source>
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| Clean a third time.
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| <figure id="Imaging-tutorial-selfcal-3.png">
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| [[File:Imaging-tutorial-selfcal-3.png|thumb|<caption>Residuals from the third clean after 2 main cycles of cleaning. </caption>]]
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| </figure>
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| <source lang="python">
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| # In CASA
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| os.system('rm -rf third_image.*')
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| tclean(vis='sis14_twhya_selfcal_2.ms',
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| imagename='third_image',
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| field='5',
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| spw='',
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| specmode='mfs',
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| deconvolver='hogbom',
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| nterms=1,
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| gridder='standard',
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| imsize=[250,250],
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| cell=['0.1arcsec'],
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| weighting='natural',
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| threshold='0mJy',
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| interactive=True,
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| niter=5000,
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| savemodel='modelcolumn')
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| </source>
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| The improvement is really marginal at this point.
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| Confident that we have done what we can on the phase, we can experiment with amplitude
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| self calibration. This is potentially dangerous as it has much more
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| potential to change the characteristics of the source than phase
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| self-calibration. We mitigate this somewhat by setting solnorm=True,
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| so that the solutions are normalized.
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| <figure id="Imaging-tutorial-selfcal-plotcal-3.png">
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| [[File:Imaging-tutorial-selfcal-plotcal-3.png|thumb|<caption>Amplitude solutions after the third round of self calibration.</caption>]]
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| </figure>
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| <source lang="python">
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| # In CASA
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| os.system("rm -rf amp.cal")
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| gaincal(vis="sis14_twhya_selfcal_2.ms",
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| caltable="amp.cal",
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| field="5",
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| solint="30s",
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| calmode="ap",
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| refant="DV22",
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| gaintype="G",
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| solnorm=True)
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| # Plot the amplitude solutions.
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| plotcal(caltable="amp.cal",
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| xaxis="time",
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| yaxis="amp",
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| subplot=331,
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| iteration="antenna",
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| plotrange=[0,0,0,0],
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| markersize=5,
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| fontsize=10.0,
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| showgui = True)
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| </source>
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| We see a good deal of scatter and some offsets between
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| correlations. It is at least worth looking at what the effects of
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| applying this will be. So let's apply these solutions on an interim basis.
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| <source lang="python">
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| # In CASA
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| applycal(vis="sis14_twhya_selfcal_2.ms",
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| field="5",
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| gaintable=["amp.cal"],
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| interp="linear")
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| </source>
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| At this point the self-calibrated data live in the corrected
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| column. Because we will want to try more rounds of self calibration,
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| it's very useful (though not strictly necessary) at this point to
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| split out the corrected data into a new data set.
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| <source lang="python">
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| # In CASA
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| os.system("rm -rf sis14_twhya_selfcal_3.ms sis14_twhya_selfcal_3.ms.flagversions")
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| split(vis="sis14_twhya_selfcal_2.ms",
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| outputvis="sis14_twhya_selfcal_3.ms",
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| datacolumn="corrected")
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| </source>
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| Clean a fourth time.
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| <figure id="Imaging-tutorial-selfcal-4.png">
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| [[File:Imaging-tutorial-selfcal-4.png|thumb|<caption>Residuals from the fourth tclean after 4 main cycles of cleaning. </caption>]]
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| </figure>
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| <source lang="python">
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| # In CASA
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| os.system('rm -rf fourth_image.*')
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| tclean(vis='sis14_twhya_selfcal_3.ms',
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| imagename='fourth_image',
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| field='5',
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| spw='',
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| specmode='mfs',
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| deconvolver='hogbom',
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| nterms=1,
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| gridder='standard',
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| imsize=[250,250],
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| cell=['0.1arcsec'],
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| weighting='natural',
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| threshold='0mJy',
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| interactive=True,
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| niter=5000)
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| </source>
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| This time, notice from the residuals that you can clean more deeply. After 4 main cycles, the background residuals look very random on
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| the scale of the beam size. This is good!
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| Compare the third and fourth images. The noise level is dramatically better,
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| while the flux has not changed markedly (this is very
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| good, it's what we worry about with amplitude self calibration).
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| By assuming that the previous cleans represent good models we have
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| managed to improve the signal-to-noise on the data by almost an
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| order of magnitude. Not bad!
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| This fourth image is our best continuum image. We can use the data
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| set (sis14_twhya_selfcal_3.ms) to proceed with later work. In the next lesson we'll do UV
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| continuum subtraction and line imaging.
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| (ASIDE: Note that you would need to do the primary beam correction on this data
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| in the same way as you corrected the previous continuum image before
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| making science measurements).
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