VLA Data Combination-CASA4.6.0: Difference between revisions

From CASA Guides
Jump to navigationJump to search
← Older edit
Jott (talk | contribs)
operators
4,947 edits
Jott (talk | contribs)
operators
4,947 edits
No edit summary
 
(50 intermediate revisions by the same user not shown)
Line 1: Line 1:
[https://casaguides.nrao.edu/index.php?title=VLA_Data_Combination-CASA5.0.0]


'''This tutorial was created and tested using CASA 4.6.0'''
'''This tutorial was created and tested using CASA 4.6.0'''
Line 4: Line 6:
== Introduction ==
== Introduction ==


The VLA can be configured into [https://science.nrao.edu/facilities/vla/docs/manuals/oss/performance/resolution four principal array configurations, A, B, C, and D.] A is the most extended and D the most compact configuration. Consequently, A configuration data has the highest spatial resolution whereas D delivers the best surface brightness sensitivity and also images the largest scales on the sky distribution. The best possible picture of an object is to combine different array combinations.  
A perfect image requires measurement of all spatial scales, which is equivalent to filling in the complete uv plane. Unfortunately, this can never be achieved with an aperture synthesis interferometer although a large number of baselines, long integration times, and multi-frequency-synthesis are all good approaches to increase the uv-coverage. One method to obtain more baselines is to observe in different array configurations and to combine the data afterwards. Deconvolution algorithms are then given good starting conditions to interpolate over the gaps in the uv-plane to achieve an improved image - an image that combines the surface brightness sensitivity of the compact baselines with the spatial resolution of the extended baselines. :
The VLA can be configured into [https://go.nrao.edu/vla-res four principal array configurations, A, B, C, and D.] A is the most extended and D is the most compact configuration. Consequently, A configuration data exhibits the highest spatial resolution whereas D configuration delivers the best surface brightness sensitivity and also images the largest scales of the sky brightness distribution.  


In this tutorial, we will combine data from the surroundings of Sgr A*, the central, supermassive black hole of our Milky Way.
In this tutorial, we will combine data from three VLA configurations (A & B & C) that were obtained in a monitoring campaign of Sgr A*, the central supermassive black hole of our Milky Way.


== Typical Observation times ==
== Typical Observing Times ==


When an object is being observed by the VLA in different configurations, ideally integration times are matched by their surface brightness sensitivity. Adjacent VLA configurations result in synthesized beams that differ in linear size by are approximately a factor of 3. The beam area is thus about 10 times different and the more extended configuration would ideally need to be 10 times longer than the most compact one. This, however, is frequently not very practical and it turns out that integration times that differ by factors of 3 are delivering data that can be combined quite well as it matches the sensitivity of overlapping VLA visibilities when data are convcolved to the same scales.  
When an object is being observed by the VLA in different array configurations, integration times are ideally matched to reach a common surface brightness sensitivity for all scales. Adjacent VLA configurations result in synthesized beams that differ in linear size by approximately a factor of 3. The beam areas therefore change by factors of ~10 and the more extended configuration would ideally need to have 10 times more integration time than the next compact one. This, however, is frequently not very practical and it turns out that integration times that differ by factors of ~3 are delivering data that can be combined satisfactorily as it matches the sensitivity of overlapping VLA visibilities when data are convolved to the same scales.  


This rule, however, is only a guidance and any data that is being obtained can be combined. Weighting will then be primarily achieved by the image "Briggs" scheme that produces weights between the "natural" and "uniform" extremes, i.e. between weighting by the number of visibilities that are gridded in each cell and weighting by the cells themselves.
This rule, however, is only a guidance and any data that are being obtained from any configuration can be combined although overlapping uv-coverages are strongly recommended. Weighting will be primarily achieved during imaging by the [https://casa.nrao.edu/casadocs/stable/synthesis-imaging/data-weighting "Briggs"] scheme that allows one to adjust imaging weights between the "natural" and "uniform" extremes, i.e., between weighting by the number of visibilities that are gridded in each cell and weighting by the cells themselves.


In addition, each visibility exhibits weights that should only depend on integration time, bandwidth, and system temperature. The VLA, however, currently does not measure Tsys and weights between different observations will need to be adjusted as described below.
In addition, each visibility exhibits weights that should only depend on integration time, bandwidth, and system temperature. The VLA, however, currently does not measure Tsys. Visibility weights between different observations will therefore need to be adjusted as described below before they can be combined.


== The data ==
== The Data ==


In the following we will combine three different datasets from the [[https://science.nrao.edu/science/service-observing NRAO Monitoring of the Galactic Center/G2 Cloud Encounter]]. We will combine S-band A, B, and C configuration data. At this stage, the data were all calibrated and the science target split out.  
In the following we will combine three different datasets from the [https://science.nrao.edu/science/service-observing NRAO Monitoring of the Galactic Center/G2 Cloud Encounter]. We will combine S-band A, B, and C configuration data. The data were all calibrated and the science target split out.  


The measurement sets can be downloaded here:  
The calibrated measurement sets can be downloaded here:  
 
[https://casa.nrao.edu/Data/EVLA/SgrA/VLA-combination-SgrA-files.tar.gz https://casa.nrao.edu/Data/EVLA/SgrA/VLA-combination-SgrA-files.tar.gz] (1.4GB)
[https://casa.nrao.edu/Data/EVLA/SgrA/VLA-combination-SgrA-files.tar.gz https://casa.nrao.edu/Data/EVLA/SgrA/VLA-combination-SgrA-files.tar.gz] (1.4GB)




Line 32: Line 34:
</source>
</source>


There will now be three unpacked MeasurementSets, one for each configuration:  
This will unpack three MeasurementSets, one for each array configuration:  


<pre>
<pre>
Line 44: Line 46:
== Initial Imaging ==
== Initial Imaging ==


To start with, we will make first, quick images to check the integrity of the data.  
We will inspect the data and create separate images to better understand the image parameters such as integration time, resolution, and rms.  


Start CASA as usual via  
Start CASA as usual via  
Line 53: Line 55:
</source>
</source>


To start with, let's have a look at the {{listobs}} output for the different files. For example, the A-configuration data had the following structure:  
As a first step, let's have a look at the {{listobs}} output for the different MeasurementSets. For example, the A-configuration MS has the following structure:  


<source lang="python">
<source lang="python">
Line 150: Line 152:
</pre>  
</pre>  


We see that A-configuration contains only the central source, "J1745-2900" which is a different name for Sgr A*. The integration time spans only a five minutes and the 15 spectral windows span a frequency range from 1.988 to 4.012GHz (when adding the bandwidth to the first channel of the last subband). Inspection of the other configuration files show almost identical setups. Although the integration times to not conform to what we discussed earlier, the data can still be combined.  
We see that A-configuration contains only the central source, "J1745-2900", which is just a different name for Sgr A*. The integration time amounts to only six minutes and the 16 spectral windows span a frequency range from 1.988 to 4.012GHz. Inspection of the other array configuration files show almost identical setups. Although the integration times between the different array configurations do not follow the 1:3:9 ratios that we discussed in the previous section, we can still combine the data without any problems. In the end, having better signal to noise on the shorter baselines can only improve the overall, combined image.  


Let's check the uv-coverages of the three datasets.
To better understand the data, let's check the uv-coverage of each of the three datasets:


<source lang="python">
<source lang="python">
Line 165: Line 167:
plotuv(vis='VLA-SgrA-Sband-Cconfig.ms')
plotuv(vis='VLA-SgrA-Sband-Cconfig.ms')
</source>
</source>
The uv-coverage plots are shown in Fig. 1. The u:v aspect ratio of each uv-coverage is high as Sgr A* is a very southern source. We also see that the longest baseline in each array differs by about a factor of 3 between the three configurations, as expected from the VLA A, B, and C configurations.


{|
{|
|[[Image:VLA-comb-A-uvcover_fld0.png|400px|thumb|left|'''Figure XX''' <br />UV-coverage of A-configuration data.]]
|[[Image:VLA-comb-A-uvcover_fld0.png|400px|thumb|left|'''Figure 1a:''' <br />UV-coverage of A-configuration data.]]
|[[Image:VLA-comb-B-uvcover_fld0.png|400px|thumb|left|'''Figure XX''' <br />UV-coverage of B-configuration data.]]
|[[Image:VLA-comb-B-uvcover_fld0.png|400px|thumb|left|'''Figure 1b:''' <br />UV-coverage of B-configuration data.]]
|[[Image:VLA-comb-C-uvcover_fld0.png|400px|thumb|left|'''Figure XX''' <br />UV-coverage of C-configuration data.]]
|[[Image:VLA-comb-C-uvcover_fld0.png|400px|thumb|left|'''Figure 1c:''' <br />UV-coverage of C-configuration data.]]
|}
|}
The next step is to determine the image quality, the synthesized beam, and the rms of each image. So we will performs simple imaging with 1000 iterations on each MS.


To make things easy, we will use the same, common cell size of 0.1 arcsec and the same image size for each configuration.
 
The next step is to determine the image quality, the synthesized beam, and the rms of each image. To do so, the images do not have to be perfectly deconvolved as we only would like to see how the combination will improve over the individual arrays.
 
To keep things simple, we will use common cell sizes of 0.1 arcsec, images of 1280 pixels on each side, and we will {{clean}} 1000 iterations (see the [http://casaguides.nrao.edu/index.php/VLA_CASA_Imaging VLA CASA imaging guide for more sophisticated imaging parameter choices]):
   
   
<source lang="python">
<source lang="python">
# In CASA
# In CASA
# A-config:  
# A-config:  
clean(vis='VLA-SgrA-Sband-Aconfig.ms', imagename='SgrA-Aonly',field='J1745-2900',mode='mfs',cell='0.1arcsec',imsize=[1280,1280],niter=1000,weighting='briggs',robust=0)
clean(vis='VLA-SgrA-Sband-Aconfig.ms', imagename='SgrA-Aonly',field='J1745-2900',
      mode='mfs',cell='0.1arcsec',imsize=[1280,1280],niter=1000,weighting='briggs',robust=0)
#
#
# B-config
# B-config
clean(vis='VLA-SgrA-Sband-Bconfig.ms', imagename='SgrA-Bonly',field='J1745-2900',mode='mfs',cell='0.1arcsec',imsize=[1280,1280],niter=1000,weighting='briggs',robust=0)
clean(vis='VLA-SgrA-Sband-Bconfig.ms', imagename='SgrA-Bonly',field='J1745-2900',
      mode='mfs',cell='0.1arcsec',imsize=[1280,1280],niter=1000,weighting='briggs',robust=0)
#
#
# C-config:
# C-config:
clean(vis='VLA-SgrA-Sband-Cconfig.ms', imagename='SgrA-Conly',field='J1745-2900',mode='mfs',cell='0.1arcsec',imsize=[1280,1280],niter=1000,weighting='briggs',robust=0)
clean(vis='VLA-SgrA-Sband-Cconfig.ms', imagename='SgrA-Conly',field='J1745-2900',
      mode='mfs',cell='0.1arcsec',imsize=[1280,1280],niter=1000,weighting='briggs',robust=0)
</source>
</source>


The clean images are not yet ideal. In particular, there are streaks across the entire image. After comparing their orientation with the psf, it is clear that they result from imperfect uv-coverage due to the short integration times of the observations. To get better images, we recommend the setting of clean boxes in the ''interactive=True'' interactive mode.
The clean images, shown in Fig. 2, give a first impression of the data. Note that when combining the images, we will improve on the uv-coverage and will therefore not only combine high resolution with high surface brightness sensitivity on large scales, but also expect a higher image fidelity, i.e. fewer artifacts due to better deconvolution.  


{|
{|
|[[Image:VLA-comb-Aonly-image.png|400px|thumb|left|'''Figure XX''' <br />Simple image of A-configuration data.]]
|[[Image:VLA-comb-Aonly-image.png|400px|thumb|left|'''Figure 2a:''' <br />Simple image of A-configuration data.]]
|[[Image:VLA-comb-Bonly-image.png|400px|thumb|left|'''Figure XX''' <br />Simple image of B-configuration data.]]
|[[Image:VLA-comb-Bonly-image.png|400px|thumb|left|'''Figure 2b:''' <br />Simple image of B-configuration data.]]
|[[Image:VLA-comb-Conly-image.png|400px|thumb|left|'''Figure XX''' <br />Simple image of C-configuration data.]]
|[[Image:VLA-comb-Conly-image.png|400px|thumb|left|'''Figure 2c:''' <br />Simple image of C-configuration data.]]
|}
|}




For the basic parameters, we find the following:
The basic parameters of the individual images can be checked through {{imhead}} for the spatial resolution, and by determining the rms in the viewer when checking the statistics of empty areas. The central point source, Sgr A*, is variable and we see this reflected in the flux density values from the images taken in the three different configurations, which were separated in observing time by several months:


For A-configuration the synthesized beam is: 1.44" x 0.40" , the rms: ~0.8mJy, and the peak flux of Sgr A* is: 0.712 Jy
A-configuration: synthesized beam 1.44" x 0.40"; rms ~0.8mJy; peak flux density of Sgr A* 0.712 Jy
 
B-configuration: synthesized beam 4.33" x 1.34"; rms ~0.7mJy; peak flux density of Sgr A* 0.691 Jy
For B-configuration the synthesized beam is: 4.33" x 1.34" , the rms: ~0.7mJy, and the peak flux of Sgr A* is: 0.691 Jy
C-configuration: synthesized beam 11.02" x 4.20"; rms ~0.6mJy; peak flux density of Sgr A* 1.35 Jy
 
For C-configuration the synthesized beam is: 11.02" x 4.20" , the rms: ~0.6mJy, and the peak flux of Sgr A* is: 1.35 Jy
 
Although the cleaning could have been better, the image quality of all three datasets is good enough for combination. In fact, adding all uv-coverages together will increase the image quality substantially.


== Check and Adjust the Visibility Weights ==
== Check and Adjust the Visibility Weights ==


The VLA does not measure system temperatures and does not have calibrated weights. However, the relative sensitivity within an observation is measured by the gain, so weights of a continuous observation are consistent. It is important though to adjust the weights between different observations. The task {{statwt}} in CASA will recalculate the visibility weights based on their rms. This task needs to be executed on each visibility dataset. Typically the default setting works quite well for continuum observations. Note that for spectral line data one should specify the ''fitspw'' parameter to exclude the line from the calculations as it will be down-weighes otherwise.  
The VLA does not measure system temperatures and does not produce calibrated, absolute weights.
VLA weights are currently only based on channel width and integration time. In the future, the VLA may use the switched power measurements to derive absolute calibrated weights. At the moment, however, the VLA weights need to be taken as being relative to each other. The relative sensitivity within an observation is measured by the gain, so weights of single, continuous observation are self-consistent. It is important, however, to adjust the weights between ''separate'' observations as they will be on different scales.  


Let's have a look at the weights before running {{statwt}}:
Let's have a look at the weights of the different datasets. We will plot the weights as a function of uv-distance, average over all channels in one spectral window, and will colorize by antenna:


<source lang="python">
<source lang="python">
# In CASA
# In CASA
# A-config:  
# A-config:  
plotms(vis='VLA-SgrA-Sband-Aconfig.ms',spw='3',averagedata=T,avgchannel='128',xaxis='uvwave',yaxis='wt',coloraxis='antenna1')
plotms(vis='VLA-SgrA-Sband-Aconfig.ms',spw='3',averagedata=T,
      avgchannel='64',xaxis='uvwave',yaxis='wt',coloraxis='antenna1')
#
#
# B-config
# B-config
plotms(vis='VLA-SgrA-Sband-Bconfig.ms',spw='3',averagedata=T,avgchannel='128',xaxis='uvwave',yaxis='wt',coloraxis='antenna1')
plotms(vis='VLA-SgrA-Sband-Bconfig.ms',spw='3',averagedata=T,
      avgchannel='64',xaxis='uvwave',yaxis='wt',coloraxis='antenna1')
#
#
# C-config:
# C-config:
plotms(vis='VLA-SgrA-Sband-Cconfig.ms',spw='3',averagedata=T,avgchannel='128',xaxis='uvwave',yaxis='wt',coloraxis='antenna1')
plotms(vis='VLA-SgrA-Sband-Cconfig.ms',spw='3',averagedata=T,
      avgchannel='64',xaxis='uvwave',yaxis='wt',coloraxis='antenna1')
</source>
</source>


Fig. 3 shows that there are some differences. Data from configurations A and B have weights that are reasonably consistent, C-configuration data, however, seems to have consistently lower values. 


{|
{|
|[[Image:VLA-comb-Awt.png|400px|thumb|left|'''Figure XX''' <br />A-config Original Weights.]]
|[[Image:VLA-comb-Awt.png|400px|thumb|left|'''Figure 3a:''' <br />A-config original weights.]]
|[[Image:VLA-comb-Bwt.png|400px|thumb|left|'''Figure XX''' <br />B-config Original Weights.]]
|[[Image:VLA-comb-Bwt.png|400px|thumb|left|'''Figure 3b:''' <br />B-config original weights.]]
|[[Image:VLA-comb-Cwt.png|400px|thumb|left|'''Figure XX''' <br />C-config Original Weights.]]
|[[Image:VLA-comb-Cwt.png|400px|thumb|left|'''Figure 3c:''' <br />C-config original weights.]]
|}
|}


<source lang="python">
# In CASA
# A-config:
plotms(vis='VLA-SgrA-Sband-Aconfig-statwt.ms',spw='3',averagedata=T,avgchannel='128',xaxis='uvwave',yaxis='wt',coloraxis='antenna1')
#
# B-config
plotms(vis='VLA-SgrA-Sband-Bconfig-statwt.ms',spw='3',averagedata=T,avgchannel='128',xaxis='uvwave',yaxis='wt',coloraxis='antenna1')
#
# C-config:
plotms(vis='VLA-SgrA-Sband-Cconfig-statwt.ms',spw='3',averagedata=T,avgchannel='128',xaxis='uvwave',yaxis='wt',coloraxis='antenna1')
</source>


The task {{statwt}} in CASA will recalculate the visibility weights based on their rms. {{statwt}} is part of the [https://go.nrao.edu/vla-pipe VLA pipeline], which was used to calibrate the original data. This explains the similarity of the A and B configuration weights in Fig. 3. But we nevertheless will run {{statwt}} once more on all files in order to ensure proper compatibility of the data (older versions of CASA may have been used for the pipeline, and CASA underwent major changes in its [https://casa.nrao.edu/casadocs/stable/reference-material/data-weights definition of data weights]).
{{statwt}} needs to be executed on each MS. The default setting calculates the weight based on the rms of each scan and spectral window. This setting works quite well for continuum observations. We would like to note though that for spectral line data the ''fitspw'' parameter should be set to exclude the line from the calculations. Otherwise, strong lines will be down-weighted.


We will use the default setting of {{statwt}} (calculation for each spw and scan per baseline). As {{statwt}} will overwrite the WEIGHT column, we first will create copies of our data:
As {{statwt}} will overwrite the WEIGHT column, we first create copies of our MSs in case we will have to revert to the original data:


<source lang="bash">
<source lang="bash">
Line 253: Line 255:
</source>
</source>
   
   
Now, we execute {{statwt}} on the new datasets:


<source lang="python">
<source lang="python">
Line 265: Line 268:
statwt(vis='VLA-SgrA-Sband-Cconfig-statwt.ms')
statwt(vis='VLA-SgrA-Sband-Cconfig-statwt.ms')
</source>
</source>
CASA will come up with a message that the CORRECTED column is not found and that DATA is being used. This is fine as indeed the MS only contains a DATA column as CORRECTED was {{split}} out after calibration, which then populated DATA in the new MS.
Let's have a look at the weight values again to see if {{statwt}} has adjusted the weights properly:
<source lang="python">
# In CASA
# A-config:
plotms(vis='VLA-SgrA-Sband-Aconfig-statwt.ms',spw='3',averagedata=T,
      avgchannel='64',xaxis='uvwave',yaxis='wt',coloraxis='antenna1')
#
# B-config
plotms(vis='VLA-SgrA-Sband-Bconfig-statwt.ms',spw='3',averagedata=T,
      avgchannel='64',xaxis='uvwave',yaxis='wt',coloraxis='antenna1')
#
# C-config:
plotms(vis='VLA-SgrA-Sband-Cconfig-statwt.ms',spw='3',averagedata=T,
      avgchannel='64',xaxis='uvwave',yaxis='wt',coloraxis='antenna1')
</source>
In Fig. 4 we plot the new, recalculated weights. The absolute scaling has indeed changed, likely due to a different weight definition that was used in earlier versions of CASA when the data were calibrated. The relative weights between A and B configurations still appear to be similar to the original, relative numbers. The C-configuration weights, however, appear to be slightly elevated relative to A and B.


{|
{|
|[[Image:VLA-comb-Astatwt.png|400px|thumb|left|'''Figure XX''' <br />A-config Original Weights.]]
|[[Image:VLA-comb-Astatwt.png|400px|thumb|left|'''Figure 4a:''' <br />A-config recalculated weights.]]
|[[Image:VLA-comb-Bstatwt.png|400px|thumb|left|'''Figure XX''' <br />B-config Original Weights.]]
|[[Image:VLA-comb-Bstatwt.png|400px|thumb|left|'''Figure 4b:''' <br />B-config recalculated weights.]]
|[[Image:VLA-comb-Cstatwt.png|400px|thumb|left|'''Figure XX''' <br />C-config Original Weights.]]
|[[Image:VLA-comb-Cstatwt.png|400px|thumb|left|'''Figure 4c:''' <br />C-config recalculated weights.]]
|}
|}


Line 276: Line 300:
'''This step is only necessary for our case which includes a variable source. In most cases, one can skip this step.'''  
'''This step is only necessary for our case which includes a variable source. In most cases, one can skip this step.'''  


As we have seen in the initial imaging step, Sgr A* is a variable sourcce. Unfortunately, this also introduces some problems with the visibilities as the different uv points will then also show different values. Cleaning will be difficult in such a situation. We therefore will remove Sgr A* in each dataset and intrduce an averaged value later.
As we have seen in the initial images, Sgr A* is a variable source. Unfortunately, this also introduces some problems for data combination as the visibilities will reflect this difference. Cleaning will be difficult in such a situation as flux levels at different uv points (times and baselines) are not consistent in their portion for the central point source. We therefore will remove the unresolved Sgr A* in each dataset and insert a new point source with a consistent, nominal flux value.  


This will be done by creating a component list, that includes only a point source at the position of Sgr A*. The component list will then be inserted into the respective MS into the model column via {{ft}}, and {{uvsub}} will subtract the point source model from the CORRECTED data column. Since there's no CORRECTED column to start with, {{uvsub}} will create it. Note that that imples that running {{uvsub}} twice will ''oversubtract''. So we recommend to again, make copies of the previous datasets.
To start with, we create a component list for each dataset that includes only a point source at the position of Sgr A* (J2000 RA=17:45:40.03599, DEC=-29:00:28.1699). The component list will be inserted as a model column into the respective MS via {{ft}}. {{uvsub}} will then subtract the point source model from the CORRECTED data column. Since there's no CORRECTED column to start with, {{uvsub}} will initially copy the DATA column to the CORRECTED column. Note that an implication of this procedures is that running {{uvsub}} twice will ''oversubtract''. So we recommend to once more create backup copies of the previous datasets before subtracting Sgr A*:


<source lang="bash">
<source lang="bash">
Line 288: Line 312:




Let's start with the A-configuration data and create a component list:
Let's start with the A-configuration data and create a component list of a point sources with a flux of 0.712 Jy at a position of (J2000) RA=17h45m40.03599s DEC=-29d00m28.1699s (more examples are provided in [https://casaguides.nrao.edu/index.php?title=Create_a_Component_List_for_Selfcal a different CASA guide]):


<source lang="python">
<source lang="python">
# In CASA
# In CASA
cl.addcomponent(flux=0.712, fluxunit='Jy',shape='point', dir='J2000 17h45m40.038s -29d00m28.07s')
cl.addcomponent(flux=0.712, fluxunit='Jy',shape='point', dir='J2000 17:45:40.03599 -29.00.28.1699')
cl.rename('component-SgrA-A.cl')
cl.rename('component-SgrA-A.cl')
cl.close()
cl.close()
</source>
</source>


Now we have a file 'component-SgrA-A.cl' that we attach to the model column of the file:
The component list is now stored in 'component-SgrA-A.cl' and we will use it to populate the MODEL column via {{ft}}:


<source lang="python">
<source lang="python">
Line 304: Line 328:
</source>
</source>


And finally we will subtract the model from the data:
Finally we will subtract the MODEL from the DATA/CORRECTED DATA with {{uvsub}}:


<source lang="python">
<source lang="python">
Line 311: Line 335:
</source>
</source>


Note that to revert back to the original state, one could {{split}} out the DATA column and strip the MODEL and the point source subtracted CORRECTED column.  
Note that to revert back to the original state, one could use {{clearcal}} to reset the MODEL column and to set CORRECTED to be identical with DATA (thus undoing the source subtraction).


Let's repeat the steps for B-configuration:
Let's repeat the steps for B-configuration, now using a flux of 0.691 Jy:


<source lang="python">
<source lang="python">
# In CASA
# In CASA
cl.addcomponent(flux=0.691, fluxunit='Jy',shape='point', dir='J2000 17h45m40.038s -29d00m28.07s')
cl.addcomponent(flux=0.691, fluxunit='Jy',shape='point', dir='J2000 17:45:40.03599 -29.00.28.1699')
cl.rename('component-SgrA-B.cl')
cl.rename('component-SgrA-B.cl')
cl.close()
cl.close()
Line 326: Line 350:
</source>
</source>


And for C-configuration:
And for C-configuration with 1.35 Jy:


<source lang="python">
<source lang="python">
# In CASA
# In CASA
cl.addcomponent(flux=1.35, fluxunit='Jy',shape='point', dir='J2000 17h45m40.038s -29d00m28.07s')
cl.addcomponent(flux=1.35, fluxunit='Jy',shape='point', dir='J2000 17:45:40.03599 -29.00.28.1699')
cl.rename('component-SgrA-C.cl')
cl.rename('component-SgrA-C.cl')
cl.close()
cl.close()
Line 340: Line 364:




Finally, we will add back in a point source with a canonical 1Jy to bring back Sgr A*. ''reverse=T'' will add instead of subtract the model in {{uvsub}}.
Finally, we will add in a point source with a canonical flux density of 1 Jy to bring back Sgr A*. ''reverse=T'' will add instead of subtract the MODEL to DATA/CORRECTED in {{uvsub}}.


<source lang="python">
<source lang="python">
# In CASA
# In CASA
cl.addcomponent(flux=1, fluxunit='Jy',shape='point', dir='J2000 17h45m40.038s -29d00m28.07s')
cl.addcomponent(flux=1, fluxunit='Jy',shape='point', dir='J2000 17:45:40.03599 -29.00.28.1699')
cl.rename('component-SgrA.cl')
cl.rename('component-SgrA.cl')
cl.close()
cl.close()
Line 352: Line 376:
ft(vis='VLA-SgrA-Sband-Aconfig-statwt-sub.ms', complist='component-SgrA.cl')
ft(vis='VLA-SgrA-Sband-Aconfig-statwt-sub.ms', complist='component-SgrA.cl')
#
#
uvsub(vis='VLA-SgrA-Sband-Bconfig-statwt-sub.ms',reverse=T)
uvsub(vis='VLA-SgrA-Sband-Aconfig-statwt-sub.ms',reverse=T)
#
#
# B-config
# B-config
Line 364: Line 388:
ft(vis='VLA-SgrA-Sband-Cconfig-statwt-sub.ms', complist='component-SgrA.cl')
ft(vis='VLA-SgrA-Sband-Cconfig-statwt-sub.ms', complist='component-SgrA.cl')
#
#
uvsub(vis='VLA-SgrA-Sband-Bconfig-statwt-sub.ms',reverse=T)
uvsub(vis='VLA-SgrA-Sband-Cconfig-statwt-sub.ms',reverse=T)
</source>
</source>


== Image Combined Data ==
== Image Combined Data ==


We can now make a combined image of all thre re-weighted datasets. The data could be combined with {{concat}}, but this is in fact not needed. {{clean}} will take care of the combination. By default, {{clean}} will image the data in the CORRECTED column, i.e. it will use the data where we subtracted the point source earlier.
We will now create a combined image of all three re-weighted datasets.  
 
To start with, we will just fill in the uv-plane, all relative weighting between the different array configurations will be performed with the robust weights. Note that the flux value for Sgr A* will also be a weighted mean of the three different datasets.
 
First, let's check the new uv-coverage. TO do so, we need to concatenate the data:


First, let's check the new uv-coverage. To do so, we need to concatenate the data with {{concat}}:


<source lang="python">
<source lang="python">
# In CASA
# In CASA
concat(vis=['VLA-SgrA-Sband-Aconfig-statwt.ms','VLA-SgrA-Sband-Bconfig-statwt.ms','VLA-SgrA-Sband-Cconfig-statwt.ms'],concatvis='VLA-SgrA-Sband-combined.ms')
concat(vis=['VLA-SgrA-Sband-Aconfig-statwt-sub.ms','VLA-SgrA-Sband-Bconfig-statwt-sub.ms','VLA-SgrA-Sband-Cconfig-statwt-sub.ms'],concatvis='VLA-SgrA-Sband-combined.ms')
#
plotuv(vis='VLA-SgrA-Sband-combined.ms')
</source>
</source>


 
Fig. 5 shows the combined uv-coverage, which extends to A-configuration baselines but with a much higher density at the intermediate and shorter baselines contributed from B and C configurations.  
<source lang="python">
# In CASA
plotuv(vis='VLA-SgrA-Sband-combined.ms')
</source>


{|
{|
|[[Image:VLA-comb-ABC-uvcover_fld0.png|400px|thumb|left|'''Figure XX''' <br />Combined uv-coverage.]]
|[[Image:VLA-comb-ABC-uvcover_fld0.png|400px|thumb|left|'''Figure 5:''' <br />Combined uv-coverage.]]
|}
|}




{{clean}} also accepts multiple files. We could use the concatenated visibilities that we just created, but let's simply use all three measurement sets for input.  
Although {{concat}} merges all three MSs into a single one, it is actually not a required step before imaging. {{clean}} will take care of the combination when all datasets are specified as a list. By default, {{clean}} will image the data in the CORRECTED column, i.e. it will use the portion of the MS which exhibits the replaced Sgr A* point source (if CORRECTED is not present, it will image the visibilities stored in the DATA column). Clean will also perform the spectral regridding of all datasets on the fly, when ''mode="velocity"'' or ''mode="frequency"''. There's no need to {{cvel}} the MSs beforehand.
 
We will now create a combined image in {{clean}}. The ''threshold'' parameter was derived by a previous run of {{clean}} in which we determined the rough rms noise. For our threshold, we will use the rms noise multiplied by a factor of ~2.5:


<source lang="python">
<source lang="python">
# In CASA
# In CASA
clean(vis=['VLA-SgrA-Sband-Aconfig-statwt.ms','VLA-SgrA-Sband-Bconfig-statwt.ms','VLA-SgrA-Sband-Cconfig-statwt.ms'], \
clean(vis=['VLA-SgrA-Sband-Aconfig-statwt-sub.ms','VLA-SgrA-Sband-Bconfig-statwt-sub.ms','VLA-SgrA-Sband-Cconfig-statwt-sub.ms'],  
       imagename='SgrA-all',field='J1745-2900',mode='mfs',cell='0.1arcsec',imsize=[1280,1280],niter=5000,\
       imagename='SgrA-all',field='J1745-2900',mode='mfs',cell='0.1arcsec',imsize=[1280,1280],niter=5000,
       threshold='5mJy',weighting='briggs',robust=0)
       threshold='5mJy',weighting='briggs',robust=0)
</source>
</source>
The resulting image has a beam size of 1.58" x 0.50" (Fig. 6), very similar to the resolution of A-configuration only. This is what we want to achieve. The rms is with ~2mJy better than A-only and there are clearly many more extended spatial scales in the image. This is a pretty good combination product. 


{|
{|
|[[Image:VLA-comb-all-image.png|400px|thumb|left|'''Figure XX''' <br />Combined image.]]
|[[Image:VLA-comb-all-image.png|400px|thumb|left|'''Figure 6:''' <br />Combined image.]]
|}
|}




The resulting image has a beam size of 1.58" x 0.50", very similar to the resolution of A-confguration only. This is what we want to achieve. The rms is better than A-only and there are clearly many more scales in the image. This is pretty good combination product.  
The image can still be improved. For simplicity, we did not use any interactive cleaning in the above, but we highly recommend it for producing the final images. Improvements can also be obtained, e.g., by adjusting the image weights via the Briggs robust parameter, adding a taper, or weighting the different datasets against each other (using ''visweightscale'' in {{concat}}). Wide-band imaging and multi-scale imaging will also lead to better results. We refer to the [http://casaguides.nrao.edu/index.php/VLA_CASA_Imaging VLA CASA Imaging Guide] for more details and examples.
 
== Tips for Selfcal ==
If the source has a bright nucleus [or bright unresolved emission generally], start with the A array data, selfcal, then add B array, selfcal again, and so on. If the source is mostly diffuse, then there is not much signal in the A array data, so start with the D array, selfcal, then add C array, selfcal, and so on. Each selfcal step should be phase only first with maybe two or more iterations. At the end of each selfcal step, a phase+amplitude selfcal can be attempted, before merging in the next array configuration data. After each merge the selfcal steps can be repeated.


The image can still be improved. For simplicity, we did not use any interactive cleaning above, but we highly recommend it for the final images. Improvements can also be obtained, e.g. by adjusting the image weights (robust parameter), adding a taper, or weighting the different datasets against each other (using {{concat}}). Wide-band imaging and multi-scale imaging will also improve the results. We refer to the [http://casaguides.nrao.edu/index.php/VLA_CASA_Imaging VLA CASA Imaging Guide] for more details. 
<!--
created: Juergen Ott August 2016
-->


{{Checked 4.6.0}}
{{Checked 4.6.0}}

Latest revision as of 22:03, 28 July 2017

[1]

This tutorial was created and tested using CASA 4.6.0

Introduction

A perfect image requires measurement of all spatial scales, which is equivalent to filling in the complete uv plane. Unfortunately, this can never be achieved with an aperture synthesis interferometer although a large number of baselines, long integration times, and multi-frequency-synthesis are all good approaches to increase the uv-coverage. One method to obtain more baselines is to observe in different array configurations and to combine the data afterwards. Deconvolution algorithms are then given good starting conditions to interpolate over the gaps in the uv-plane to achieve an improved image - an image that combines the surface brightness sensitivity of the compact baselines with the spatial resolution of the extended baselines. : The VLA can be configured into four principal array configurations, A, B, C, and D. A is the most extended and D is the most compact configuration. Consequently, A configuration data exhibits the highest spatial resolution whereas D configuration delivers the best surface brightness sensitivity and also images the largest scales of the sky brightness distribution.

In this tutorial, we will combine data from three VLA configurations (A & B & C) that were obtained in a monitoring campaign of Sgr A*, the central supermassive black hole of our Milky Way.

Typical Observing Times

When an object is being observed by the VLA in different array configurations, integration times are ideally matched to reach a common surface brightness sensitivity for all scales. Adjacent VLA configurations result in synthesized beams that differ in linear size by approximately a factor of 3. The beam areas therefore change by factors of ~10 and the more extended configuration would ideally need to have 10 times more integration time than the next compact one. This, however, is frequently not very practical and it turns out that integration times that differ by factors of ~3 are delivering data that can be combined satisfactorily as it matches the sensitivity of overlapping VLA visibilities when data are convolved to the same scales.

This rule, however, is only a guidance and any data that are being obtained from any configuration can be combined although overlapping uv-coverages are strongly recommended. Weighting will be primarily achieved during imaging by the "Briggs" scheme that allows one to adjust imaging weights between the "natural" and "uniform" extremes, i.e., between weighting by the number of visibilities that are gridded in each cell and weighting by the cells themselves.

In addition, each visibility exhibits weights that should only depend on integration time, bandwidth, and system temperature. The VLA, however, currently does not measure Tsys. Visibility weights between different observations will therefore need to be adjusted as described below before they can be combined.

The Data

In the following we will combine three different datasets from the NRAO Monitoring of the Galactic Center/G2 Cloud Encounter. We will combine S-band A, B, and C configuration data. The data were all calibrated and the science target split out. 

The calibrated measurement sets can be downloaded here: 
https://casa.nrao.edu/Data/EVLA/SgrA/VLA-combination-SgrA-files.tar.gz (1.4GB)


As a first step, let's download the files, then untar: 

# In Terminal
tar -xzvf VLA-combination-SgrA-files.tar.gz

This will unpack three MeasurementSets, one for each array configuration: 

VLA-SgrA-Sband-Aconfig.ms

VLA-SgrA-Sband-Bconfig.ms

VLA-SgrA-Sband-Cconfig.ms

Initial Imaging

We will inspect the data and create separate images to better understand the image parameters such as integration time, resolution, and rms.

Start CASA as usual via 

# In Terminal
casa

As a first step, let's have a look at the listobs output for the different MeasurementSets. For example, the A-configuration MS has the following structure: 

# In CASA
listobs(vis='VLA-SgrA-Sband-Aconfig.ms')

##########################################
##### Begin Task: listobs            #####
listobs(vis="VLA-SgrA-Sband-Aconfig.ms",selectdata=True,spw="",field="",antenna="",
        uvrange="",timerange="",correlation="",scan="",intent="",
        feed="",array="",observation="",verbose=True,listfile="",
        listunfl=False,cachesize=50,overwrite=False)
================================================================================
           MeasurementSet Name:  /lustre/aoc/sciops/jott/casa/topicalguide/combination/test/VLA-SgrA-Sband-Aconfig.ms      MS Version 2
================================================================================
   Observer: lorant sjouwerman     Project: uid://evla/pdb/11434214  
Observation: EVLA
Data records: 528000       Total elapsed time = 360 seconds
   Observed from   31-May-2014/09:07:57.0   to   31-May-2014/09:13:57.0 (UTC)
Compute subscan properties
   
   ObservationID = 0         ArrayID = 0
  Date        Timerange (UTC)          Scan  FldId FieldName             nRows     SpwIds   Average Interval(s)    ScanIntent
  31-May-2014/09:07:57.0 - 09:13:57.0    63      0 J1745-2900              528000  [0,3,4,5,6,7,8,9,10,11,12,13,14,15]  [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3] [OBSERVE_TARGET#UNSPECIFIED]
           (nRows = Total number of rows per scan) 
Fields: 1
  ID   Code Name                RA               Decl           Epoch   SrcId      nRows
  0    NONE J1745-2900          17:45:40.038300 -29.00.28.06899 J2000   0         528000
Spectral Windows:  (14 unique spectral windows and 1 unique polarization setups)
  SpwID  Name           #Chans   Frame   Ch0(MHz)  ChanWid(kHz)  TotBW(kHz) CtrFreq(MHz) BBC Num  Corrs  
  0      EVLA_S#A0C0#80     64   TOPO    1988.000      2000.000    128000.0   2051.0000       12  RR  LL
  3      EVLA_S#A0C0#83     64   TOPO    2372.000      2000.000    128000.0   2435.0000       12  RR  LL
  4      EVLA_S#A0C0#84     64   TOPO    2500.000      2000.000    128000.0   2563.0000       12  RR  LL
  5      EVLA_S#A0C0#85     64   TOPO    2628.000      2000.000    128000.0   2691.0000       12  RR  LL
  6      EVLA_S#A0C0#86     64   TOPO    2756.000      2000.000    128000.0   2819.0000       12  RR  LL
  7      EVLA_S#A0C0#87     64   TOPO    2884.000      2000.000    128000.0   2947.0000       12  RR  LL
  8      EVLA_S#B0D0#88     64   TOPO    2988.000      2000.000    128000.0   3051.0000       15  RR  LL
  9      EVLA_S#B0D0#89     64   TOPO    3116.000      2000.000    128000.0   3179.0000       15  RR  LL
  10     EVLA_S#B0D0#90     64   TOPO    3244.000      2000.000    128000.0   3307.0000       15  RR  LL
  11     EVLA_S#B0D0#91     64   TOPO    3372.000      2000.000    128000.0   3435.0000       15  RR  LL
  12     EVLA_S#B0D0#92     64   TOPO    3500.000      2000.000    128000.0   3563.0000       15  RR  LL
  13     EVLA_S#B0D0#93     64   TOPO    3628.000      2000.000    128000.0   3691.0000       15  RR  LL
  14     EVLA_S#B0D0#94     64   TOPO    3756.000      2000.000    128000.0   3819.0000       15  RR  LL
  15     EVLA_S#B0D0#95     64   TOPO    3884.000      2000.000    128000.0   3947.0000       15  RR  LL
Sources: 16
  ID   Name                SpwId RestFreq(MHz)  SysVel(km/s) 
  0    J1745-2900          0     -              -            
  0    J1745-2900          1     -              -            
  0    J1745-2900          2     -              -            
  0    J1745-2900          3     -              -            
  0    J1745-2900          4     -              -            
  0    J1745-2900          5     -              -            
  0    J1745-2900          6     -              -            
  0    J1745-2900          7     -              -            
  0    J1745-2900          8     -              -            
  0    J1745-2900          9     -              -            
  0    J1745-2900          10    -              -            
  0    J1745-2900          11    -              -            
  0    J1745-2900          12    -              -            
  0    J1745-2900          13    -              -            
  0    J1745-2900          14    -              -            
  0    J1745-2900          15    -              -            
Antennas: 26:
  ID   Name  Station   Diam.    Long.         Lat.                Offset from array center (m)                ITRF Geocentric coordinates (m)        
                                                                     East         North     Elevation               x               y               z
  0    ea01  N32       25.0 m   -107.37.22.0  +33.56.33.6       -441.7442     4689.9683      -16.9356 -1600781.062100 -5039347.430600  3558761.526300
  1    ea02  N64       25.0 m   -107.37.58.7  +34.02.20.5      -1382.3632    15410.1417      -40.6233 -1599855.668100 -5033332.388100  3567636.626500
  2    ea03  E64       25.0 m   -107.27.00.1  +33.50.06.7      15507.5911    -7263.7210       67.2006 -1587600.203200 -5050575.869700  3548885.404900
  3    ea04  E24       25.0 m   -107.35.13.4  +33.53.18.1       2858.1804    -1349.1324       13.7306 -1598663.094300 -5043581.396100  3553767.023200
  4    ea05  W08       25.0 m   -107.37.21.6  +33.53.53.0       -432.1181     -272.1470       -1.5057 -1601614.092200 -5042001.651900  3554652.509800
  5    ea06  N56       25.0 m   -107.37.47.9  +34.00.38.4      -1105.2076    12254.3155      -34.2423 -1600128.382500 -5035104.142000  3565024.679400
  6    ea07  N48       25.0 m   -107.37.38.1  +33.59.06.2       -855.2644     9405.9610      -25.9303 -1600374.875000 -5036704.207500  3562667.885900
  7    ea08  N16       25.0 m   -107.37.10.9  +33.54.48.0       -155.8517     1426.6442       -9.3792 -1601061.957400 -5041175.883000  3556058.040000
  8    ea09  W64       25.0 m   -107.46.20.1  +33.48.50.9     -14240.7638    -9606.2696       17.1066 -1616361.587500 -5042770.516600  3546911.446900
  9    ea10  E40       25.0 m   -107.32.35.4  +33.52.16.9       6908.8305    -3240.7192       39.0202 -1595124.923100 -5045829.467200  3552210.703600
  10   ea11  W24       25.0 m   -107.38.49.0  +33.53.04.0      -2673.3552    -1784.5888       10.4757 -1604008.749300 -5042135.808900  3553403.716000
  11   ea12  N09       25.0 m   -107.37.07.8  +33.54.19.0        -77.4204      530.6453       -5.5755 -1601139.471200 -5041679.039700  3555316.553900
  12   ea13  W56       25.0 m   -107.44.26.7  +33.49.54.6     -11333.2004    -7637.6771       15.3707 -1613255.393400 -5042613.099800  3548545.915000
  13   ea14  E08       25.0 m   -107.36.48.9  +33.53.55.1        407.8298     -206.0320       -3.2196 -1600801.931000 -5042219.386500  3554706.431200
  14   ea15  E56       25.0 m   -107.29.04.1  +33.50.54.9      12327.6313    -5774.7469       42.6153 -1590380.611000 -5048810.243300  3550108.432300
  15   ea17  E32       25.0 m   -107.34.01.5  +33.52.50.3       4701.6413    -2209.7152       25.2066 -1597053.135800 -5044604.681200  3553058.995000
  16   ea18  E72       25.0 m   -107.24.42.3  +33.49.18.0      19041.8717    -8769.2047        4.7262 -1584460.871200 -5052385.599800  3547600.000100
  17   ea19  W16       25.0 m   -107.37.57.4  +33.53.33.0      -1348.7109     -890.6216        1.3005 -1602592.853600 -5042054.996800  3554140.704800
  18   ea20  N40       25.0 m   -107.37.29.5  +33.57.44.4       -633.6074     6878.6018      -20.7693 -1600592.756000 -5038121.357300  3560574.853200
  19   ea21  E48       25.0 m   -107.30.56.1  +33.51.38.4       9456.6097    -4431.6564       37.9341 -1592894.077600 -5047229.138200  3551221.206000
  20   ea22  N24       25.0 m   -107.37.16.1  +33.55.37.7       -290.3752     2961.8594      -12.2389 -1600930.087800 -5040316.396400  3557330.387200
  21   ea23  W72       25.0 m   -107.48.24.0  +33.47.41.2     -17419.4740   -11760.2758       14.9538 -1619757.312900 -5042937.664400  3545120.392300
  22   ea24  W48       25.0 m   -107.42.44.3  +33.50.52.1      -8707.9403    -5861.7877       15.5282 -1610451.925800 -5042471.125800  3550021.055800
  23   ea25  W32       25.0 m   -107.39.54.8  +33.52.27.2      -4359.4392    -2923.1244       11.7721 -1605808.634900 -5042230.089000  3552459.209500
  24   ea26  W40       25.0 m   -107.41.13.5  +33.51.43.1      -6377.9880    -4286.7769        8.2038 -1607962.463800 -5042338.190100  3551324.947500
  25   ea28  E16       25.0 m   -107.36.09.8  +33.53.40.0       1410.0378     -673.4764       -0.7821 -1599926.107500 -5042772.979700  3554319.790400
##### End Task: listobs              #####
##########################################

We see that A-configuration contains only the central source, "J1745-2900", which is just a different name for Sgr A*. The integration time amounts to only six minutes and the 16 spectral windows span a frequency range from 1.988 to 4.012GHz. Inspection of the other array configuration files show almost identical setups. Although the integration times between the different array configurations do not follow the 1:3:9 ratios that we discussed in the previous section, we can still combine the data without any problems. In the end, having better signal to noise on the shorter baselines can only improve the overall, combined image.

To better understand the data, let's check the uv-coverage of each of the three datasets: 

# In CASA
# A-config: 
plotuv(vis='VLA-SgrA-Sband-Aconfig.ms')
#
# B-config
plotuv(vis='VLA-SgrA-Sband-Bconfig.ms')
#
# C-config:
plotuv(vis='VLA-SgrA-Sband-Cconfig.ms')

The uv-coverage plots are shown in Fig. 1. The u:v aspect ratio of each uv-coverage is high as Sgr A* is a very southern source. We also see that the longest baseline in each array differs by about a factor of 3 between the three configurations, as expected from the VLA A, B, and C configurations.

Figure 1a:
UV-coverage of A-configuration data.
Figure 1b:
UV-coverage of B-configuration data.
Figure 1c:
UV-coverage of C-configuration data.


The next step is to determine the image quality, the synthesized beam, and the rms of each image. To do so, the images do not have to be perfectly deconvolved as we only would like to see how the combination will improve over the individual arrays.

To keep things simple, we will use common cell sizes of 0.1 arcsec, images of 1280 pixels on each side, and we will clean 1000 iterations (see the VLA CASA imaging guide for more sophisticated imaging parameter choices): 

# In CASA
# A-config: 
clean(vis='VLA-SgrA-Sband-Aconfig.ms', imagename='SgrA-Aonly',field='J1745-2900',
      mode='mfs',cell='0.1arcsec',imsize=[1280,1280],niter=1000,weighting='briggs',robust=0)
#
# B-config
clean(vis='VLA-SgrA-Sband-Bconfig.ms', imagename='SgrA-Bonly',field='J1745-2900',
      mode='mfs',cell='0.1arcsec',imsize=[1280,1280],niter=1000,weighting='briggs',robust=0)
#
# C-config:
clean(vis='VLA-SgrA-Sband-Cconfig.ms', imagename='SgrA-Conly',field='J1745-2900',
      mode='mfs',cell='0.1arcsec',imsize=[1280,1280],niter=1000,weighting='briggs',robust=0)

The clean images, shown in Fig. 2, give a first impression of the data. Note that when combining the images, we will improve on the uv-coverage and will therefore not only combine high resolution with high surface brightness sensitivity on large scales, but also expect a higher image fidelity, i.e. fewer artifacts due to better deconvolution.

Figure 2a:
Simple image of A-configuration data.
Figure 2b:
Simple image of B-configuration data.
Figure 2c:
Simple image of C-configuration data.


The basic parameters of the individual images can be checked through imhead for the spatial resolution, and by determining the rms in the viewer when checking the statistics of empty areas. The central point source, Sgr A*, is variable and we see this reflected in the flux density values from the images taken in the three different configurations, which were separated in observing time by several months: 

A-configuration: synthesized beam  1.44" x 0.40"; rms ~0.8mJy; peak flux density of Sgr A* 0.712 Jy
B-configuration: synthesized beam  4.33" x 1.34"; rms ~0.7mJy; peak flux density of Sgr A* 0.691 Jy
C-configuration: synthesized beam 11.02" x 4.20"; rms ~0.6mJy; peak flux density of Sgr A* 1.35 Jy

Check and Adjust the Visibility Weights

The VLA does not measure system temperatures and does not produce calibrated, absolute weights. VLA weights are currently only based on channel width and integration time. In the future, the VLA may use the switched power measurements to derive absolute calibrated weights. At the moment, however, the VLA weights need to be taken as being relative to each other. The relative sensitivity within an observation is measured by the gain, so weights of single, continuous observation are self-consistent. It is important, however, to adjust the weights between separate observations as they will be on different scales.

Let's have a look at the weights of the different datasets. We will plot the weights as a function of uv-distance, average over all channels in one spectral window, and will colorize by antenna: 

# In CASA
# A-config: 
plotms(vis='VLA-SgrA-Sband-Aconfig.ms',spw='3',averagedata=T,
       avgchannel='64',xaxis='uvwave',yaxis='wt',coloraxis='antenna1')
#
# B-config
plotms(vis='VLA-SgrA-Sband-Bconfig.ms',spw='3',averagedata=T,
       avgchannel='64',xaxis='uvwave',yaxis='wt',coloraxis='antenna1')
#
# C-config:
plotms(vis='VLA-SgrA-Sband-Cconfig.ms',spw='3',averagedata=T,
       avgchannel='64',xaxis='uvwave',yaxis='wt',coloraxis='antenna1')

Fig. 3 shows that there are some differences. Data from configurations A and B have weights that are reasonably consistent, C-configuration data, however, seems to have consistently lower values.

Figure 3a:
A-config original weights.
Figure 3b:
B-config original weights.
Figure 3c:
C-config original weights.


The task statwt in CASA will recalculate the visibility weights based on their rms. statwt is part of the VLA pipeline, which was used to calibrate the original data. This explains the similarity of the A and B configuration weights in Fig. 3. But we nevertheless will run statwt once more on all files in order to ensure proper compatibility of the data (older versions of CASA may have been used for the pipeline, and CASA underwent major changes in its definition of data weights).

statwt needs to be executed on each MS. The default setting calculates the weight based on the rms of each scan and spectral window. This setting works quite well for continuum observations. We would like to note though that for spectral line data the fitspw parameter should be set to exclude the line from the calculations. Otherwise, strong lines will be down-weighted.

As statwt will overwrite the WEIGHT column, we first create copies of our MSs in case we will have to revert to the original data: 

# In Linux
cp -r VLA-SgrA-Sband-Aconfig.ms VLA-SgrA-Sband-Aconfig-statwt.ms
cp -r VLA-SgrA-Sband-Bconfig.ms VLA-SgrA-Sband-Bconfig-statwt.ms
cp -r VLA-SgrA-Sband-Cconfig.ms VLA-SgrA-Sband-Cconfig-statwt.ms

Now, we execute statwt on the new datasets: 

# In CASA
# A-config: 
statwt(vis='VLA-SgrA-Sband-Aconfig-statwt.ms')
#
# B-config
statwt(vis='VLA-SgrA-Sband-Bconfig-statwt.ms')
#
# C-config:
statwt(vis='VLA-SgrA-Sband-Cconfig-statwt.ms')

CASA will come up with a message that the CORRECTED column is not found and that DATA is being used. This is fine as indeed the MS only contains a DATA column as CORRECTED was split out after calibration, which then populated DATA in the new MS.

Let's have a look at the weight values again to see if statwt has adjusted the weights properly: 

# In CASA
# A-config: 
plotms(vis='VLA-SgrA-Sband-Aconfig-statwt.ms',spw='3',averagedata=T,
       avgchannel='64',xaxis='uvwave',yaxis='wt',coloraxis='antenna1')
#
# B-config
plotms(vis='VLA-SgrA-Sband-Bconfig-statwt.ms',spw='3',averagedata=T,
       avgchannel='64',xaxis='uvwave',yaxis='wt',coloraxis='antenna1')
#
# C-config:
plotms(vis='VLA-SgrA-Sband-Cconfig-statwt.ms',spw='3',averagedata=T,
       avgchannel='64',xaxis='uvwave',yaxis='wt',coloraxis='antenna1')

In Fig. 4 we plot the new, recalculated weights. The absolute scaling has indeed changed, likely due to a different weight definition that was used in earlier versions of CASA when the data were calibrated. The relative weights between A and B configurations still appear to be similar to the original, relative numbers. The C-configuration weights, however, appear to be slightly elevated relative to A and B.

Figure 4a:
A-config recalculated weights.
Figure 4b:
B-config recalculated weights.
Figure 4c:
C-config recalculated weights.

Removal of the variable Sgr A* point source

This step is only necessary for our case which includes a variable source. In most cases, one can skip this step.

As we have seen in the initial images, Sgr A* is a variable source. Unfortunately, this also introduces some problems for data combination as the visibilities will reflect this difference. Cleaning will be difficult in such a situation as flux levels at different uv points (times and baselines) are not consistent in their portion for the central point source. We therefore will remove the unresolved Sgr A* in each dataset and insert a new point source with a consistent, nominal flux value.

To start with, we create a component list for each dataset that includes only a point source at the position of Sgr A* (J2000 RA=17:45:40.03599, DEC=-29:00:28.1699). The component list will be inserted as a model column into the respective MS via ft. uvsub will then subtract the point source model from the CORRECTED data column. Since there's no CORRECTED column to start with, uvsub will initially copy the DATA column to the CORRECTED column. Note that an implication of this procedures is that running uvsub twice will oversubtract. So we recommend to once more create backup copies of the previous datasets before subtracting Sgr A*: 

# In Linux
cp -r VLA-SgrA-Sband-Aconfig-statwt.ms VLA-SgrA-Sband-Aconfig-statwt-sub.ms
cp -r VLA-SgrA-Sband-Bconfig-statwt.ms VLA-SgrA-Sband-Bconfig-statwt-sub.ms
cp -r VLA-SgrA-Sband-Cconfig-statwt.ms VLA-SgrA-Sband-Cconfig-statwt-sub.ms


Let's start with the A-configuration data and create a component list of a point sources with a flux of 0.712 Jy at a position of (J2000) RA=17h45m40.03599s DEC=-29d00m28.1699s (more examples are provided in a different CASA guide): 

# In CASA
cl.addcomponent(flux=0.712, fluxunit='Jy',shape='point', dir='J2000 17:45:40.03599 -29.00.28.1699')
cl.rename('component-SgrA-A.cl')
cl.close()

The component list is now stored in 'component-SgrA-A.cl' and we will use it to populate the MODEL column via ft: 

# In CASA
ft(vis='VLA-SgrA-Sband-Aconfig-statwt-sub.ms', complist='component-SgrA-A.cl')

Finally we will subtract the MODEL from the DATA/CORRECTED DATA with uvsub: 

# In CASA
uvsub(vis='VLA-SgrA-Sband-Aconfig-statwt-sub.ms')

Note that to revert back to the original state, one could use clearcal to reset the MODEL column and to set CORRECTED to be identical with DATA (thus undoing the source subtraction).

Let's repeat the steps for B-configuration, now using a flux of 0.691 Jy: 

# In CASA
cl.addcomponent(flux=0.691, fluxunit='Jy',shape='point', dir='J2000 17:45:40.03599 -29.00.28.1699')
cl.rename('component-SgrA-B.cl')
cl.close()
#
ft(vis='VLA-SgrA-Sband-Bconfig-statwt-sub.ms', complist='component-SgrA-B.cl')
#
uvsub(vis='VLA-SgrA-Sband-Bconfig-statwt-sub.ms')

And for C-configuration with 1.35 Jy: 

# In CASA
cl.addcomponent(flux=1.35, fluxunit='Jy',shape='point', dir='J2000 17:45:40.03599 -29.00.28.1699')
cl.rename('component-SgrA-C.cl')
cl.close()
#
ft(vis='VLA-SgrA-Sband-Cconfig-statwt-sub.ms', complist='component-SgrA-C.cl')
#
uvsub(vis='VLA-SgrA-Sband-Cconfig-statwt-sub.ms')


Finally, we will add in a point source with a canonical flux density of 1 Jy to bring back Sgr A*. reverse=T will add instead of subtract the MODEL to DATA/CORRECTED in uvsub. 

# In CASA
cl.addcomponent(flux=1, fluxunit='Jy',shape='point', dir='J2000 17:45:40.03599 -29.00.28.1699')
cl.rename('component-SgrA.cl')
cl.close()
#
# A-config
#
ft(vis='VLA-SgrA-Sband-Aconfig-statwt-sub.ms', complist='component-SgrA.cl')
#
uvsub(vis='VLA-SgrA-Sband-Aconfig-statwt-sub.ms',reverse=T)
#
# B-config
#
ft(vis='VLA-SgrA-Sband-Bconfig-statwt-sub.ms', complist='component-SgrA.cl')
#
uvsub(vis='VLA-SgrA-Sband-Bconfig-statwt-sub.ms',reverse=T)
#
# C-config
#
ft(vis='VLA-SgrA-Sband-Cconfig-statwt-sub.ms', complist='component-SgrA.cl')
#
uvsub(vis='VLA-SgrA-Sband-Cconfig-statwt-sub.ms',reverse=T)

Image Combined Data

We will now create a combined image of all three re-weighted datasets.

First, let's check the new uv-coverage. To do so, we need to concatenate the data with concat: 

# In CASA
concat(vis=['VLA-SgrA-Sband-Aconfig-statwt-sub.ms','VLA-SgrA-Sband-Bconfig-statwt-sub.ms','VLA-SgrA-Sband-Cconfig-statwt-sub.ms'],concatvis='VLA-SgrA-Sband-combined.ms')
#
plotuv(vis='VLA-SgrA-Sband-combined.ms')

Fig. 5 shows the combined uv-coverage, which extends to A-configuration baselines but with a much higher density at the intermediate and shorter baselines contributed from B and C configurations.

Figure 5:
Combined uv-coverage.


Although concat merges all three MSs into a single one, it is actually not a required step before imaging. clean will take care of the combination when all datasets are specified as a list. By default, clean will image the data in the CORRECTED column, i.e. it will use the portion of the MS which exhibits the replaced Sgr A* point source (if CORRECTED is not present, it will image the visibilities stored in the DATA column). Clean will also perform the spectral regridding of all datasets on the fly, when mode="velocity" or mode="frequency". There's no need to cvel the MSs beforehand.

We will now create a combined image in clean. The threshold parameter was derived by a previous run of clean in which we determined the rough rms noise. For our threshold, we will use the rms noise multiplied by a factor of ~2.5: 

# In CASA
clean(vis=['VLA-SgrA-Sband-Aconfig-statwt-sub.ms','VLA-SgrA-Sband-Bconfig-statwt-sub.ms','VLA-SgrA-Sband-Cconfig-statwt-sub.ms'], 
      imagename='SgrA-all',field='J1745-2900',mode='mfs',cell='0.1arcsec',imsize=[1280,1280],niter=5000,
      threshold='5mJy',weighting='briggs',robust=0)


The resulting image has a beam size of 1.58" x 0.50" (Fig. 6), very similar to the resolution of A-configuration only. This is what we want to achieve. The rms is with ~2mJy better than A-only and there are clearly many more extended spatial scales in the image. This is a pretty good combination product.

Figure 6:
Combined image.


The image can still be improved. For simplicity, we did not use any interactive cleaning in the above, but we highly recommend it for producing the final images. Improvements can also be obtained, e.g., by adjusting the image weights via the Briggs robust parameter, adding a taper, or weighting the different datasets against each other (using visweightscale in concat). Wide-band imaging and multi-scale imaging will also lead to better results. We refer to the VLA CASA Imaging Guide for more details and examples.

Tips for Selfcal

If the source has a bright nucleus [or bright unresolved emission generally], start with the A array data, selfcal, then add B array, selfcal again, and so on. If the source is mostly diffuse, then there is not much signal in the A array data, so start with the D array, selfcal, then add C array, selfcal, and so on. Each selfcal step should be phase only first with maybe two or more iterations. At the end of each selfcal step, a phase+amplitude selfcal can be attempted, before merging in the next array configuration data. After each merge the selfcal steps can be repeated.


Last checked on CASA Version 4.6.0

Retrieved from "https://casaguides.nrao.edu/index.php?title=VLA_Data_Combination-CASA4.6.0&oldid=22179"