VLA Source Subtraction Topical Guide-CASA6.2.0: Difference between revisions

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== Introduction ==
== 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 across the gaps in the uv-plane to achieve an image that combines the surface brightness sensitivity of the compact baselines with the spatial resolution of the extended baselines.  
Occasionally, observers may find that the emission of one source overlaps with that of nearby sources. This overlap can present analytical challenges if the nearby sources, which are the targets of interest, become obscured. These problems may arise when observing crowded fields or very bright objects. By modelling the emission of the obscuring source, or sources, one can then subtract it from the dataset and thus disentangle the emission.


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 exhibit 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 subtract the variable and unresolved point source of Sgr A*, the central supermassive black hole of our Milky Way from a dataset that was obtained in a monitoring campaign. This dataset includes observations from three different configurations of the VLA. To understand how to combine these three datasets into a single dataset the user is advised to study our guide on data combination. After performing this combination the user may then refer to the final section where we will show how to insert back into the dataset a model of the subtracted source.
 
In this tutorial, we will combine data from three VLA configurations (A, B, and 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, on-source 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 deliver data that can be satisfactorily combined. This combination matches the sensitivity of overlapping VLA visibilities when the data are convolved to the same scales.
 
Although overlapping uv-coverages are essential for the best imaging, it is possible to combine non-overlapping data if one understands that some spacings are not present and that the adjustment of the individual datasets is somewhat subjective.
Weighting will be primarily achieved during imaging by the [https://casadocs.readthedocs.io/en/stable/notebooks/synthesis_imaging.html?highlight=briggs#Data-Weighting Briggs] scheme that allows one to adjust imaging weights between the natural (weighting by the number of visibilities that are gridded in each cell) and uniform (weighting by the cells themselves) extremes.
 
Additionally, each visibility exhibits weights that should only depend on correlator integration time, bandwidth, and system temperature (Tsys). Note that the VLA does not apply Tsys in the online system. Visibility weights between different observations will therefore need to be adjusted, as described below, before they can be combined.


== The Data ==
== The Data ==


We will be combining three different datasets, S-band A, B, and C configuration data, from the [https://science.nrao.edu/science/service-observing NRAO Monitoring of the Galactic Center/G2 Cloud Encounter]. The data were all calibrated and the science target [https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.manipulation.split.html split] out.  
We will be removing the point source from three different S-band datasets obtained in the A, B, and C configurations, from the [https://science.nrao.edu/science/service-observing NRAO Monitoring of the Galactic Center/G2 Cloud Encounter]. The datasets have all been calibrated and had their science targets [https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.manipulation.split.html split] out.  


  The calibrated measurement sets can be downloaded here:  
  The calibrated measurement sets can be downloaded here:  
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   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
   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
   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
   15  ea17  E32      25.0 m  -107.34.01.5  +33.52.50.3      4701.6Difference between revisions of "VLA Data Combination-CASA6.2.0"
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413   -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
   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
   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
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</pre>  
</pre>  


We see that A-configuration contains only the central source, J1745-2900, which is just a different name for Sgr A* (the target source has been [https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.manipulation.split.html?highlight=split split] out to remove all calibrators). The on-source integration time amounts to only six minutes and the 14 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 problem. In the end, having better signal to noise on the shorter baselines can only improve the overall combined image.  
We see that the target source for A-configuration is J1745-2900 (Sgr A*) (the target source has been [https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.manipulation.split.html?highlight=split split] out to remove all calibrators). The on-source integration time amounts to only six minutes and the 14 spectral windows span a frequency range from 1.988 to 4.012GHz. Inspection of the other array configuration files show almost identical setups.  


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


The next step is to determine the image quality, the synthesized beam, and the rms of each image. 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.  
The next step is to create images we will then model and subtract the point source from.  


To keep things simple, we will use common cell sizes of 0.1 arcsec, which samples well the A-configuration beam and oversamples the more compact configurations. We create images of 1280 pixels on each side, and we will [https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.imaging.tclean.html tclean] 1000 iterations (see the [http://casaguides.nrao.edu/index.php/VLA_CASA_Imaging VLA CASA imaging guide] for more sophisticated imaging parameter choices). :
To keep things simple, we will use common cell sizes of 0.1 arcsec, which samples well the A-configuration beam and oversamples the more compact configurations. We create images of 1280 pixels on each side, and we will [https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.imaging.tclean.html tclean] 1000 iterations (see the [http://casaguides.nrao.edu/index.php/VLA_CASA_Imaging VLA CASA imaging guide] for more sophisticated imaging parameter choices). :
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</source>
</source>


The clean images, shown in Fig. 2a-c, 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.  
The clean images, shown in Fig. 2a-c, give a first impression of the data.  


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== Removal of the variable Sgr A* point source ==
== Removal of the variable Sgr A* point source ==
 
We have seen in the initial images that Sgr A* is a variable source. The variability would introduces problems if a user wished to combine the datasets as the visibilities of the different sessions will reflect these variations. Cleaning would be difficult in such a situation as flux levels at different uv points (times and baselines) would not be consistent in their portion for the central point source. Therefore, we will remove the unresolved Sgr A* in each dataset. For those who are interested in having Sgr A* back in the image, we will later show how to re-introduce a nominal point source with a flux of 1Jy. It is perfectly fine, however, to image without the artificial source.  
'''This step is only necessary for our case which includes a variable source. In most cases, one can skip this step.'''
 
We have seen in the initial images that Sgr A* is a variable source. This introduces some problems for data combination as the visibilities of the different sessions will reflect these variations. 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. Therefore, we will remove the unresolved Sgr A* in each dataset. For those who are interested in having Sgr A* back in the image, we will later show how to re-introduce a nominal point source with a flux of 1Jy. It is perfectly fine, however, to image without the artificial source.  


To start, we create a component list for each dataset that includes only a point source at the position of Sgr A*. To do so, we first need to find out the position of the central point source. This is performed by fitting a point source (2d-Gaussian function) to the A-configuration data, which has the largest angular resolution. We use [https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.analysis.imfit.html imfit]:
To start, we create a component list for each dataset that includes only a point source at the position of Sgr A*. To do so, we first need to find out the position of the central point source. This is performed by fitting a point source (2d-Gaussian function) to the A-configuration data, which has the largest angular resolution. We use [https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.analysis.imfit.html imfit]:
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== Inserting an artificial Point Source for Sgr A* ==
== Inserting an artificial Point Source for Sgr A* ==
'''Prior to this step, please refer to the CASA guide on multi configuration data combination as we will now be using a combined dataset of the three configurations'''


Finally, we will add in a point source with a canonical flux density of 1 Jy to bring back Sgr A*. We again create a component list and use [https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.imaging.ft.html ft] to attach the source model. Using [https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.manipulation.uvsub.html uvsub] with ''reverse=True'' will add instead of subtract the MODEL to DATA/CORRECTED. This step can be skipped when Sgr A* is not required. When the data are going to be self-calibrated, we actually recommend to not introduce an artificial source during the calibration steps as it has no phase errors imprinted. For this example, we now re-introduce Sgr A*. Once more, we start by copying the combined datasets:  
We can also add in a point source with a canonical flux density of 1 Jy to bring back Sgr A*. We again create a component list and use [https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.imaging.ft.html ft] to attach the source model. Using [https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.manipulation.uvsub.html uvsub] with ''reverse=True'' will add instead of subtract the MODEL to DATA/CORRECTED. This step can be skipped when Sgr A* is not required. When the data are going to be self-calibrated, we actually recommend to not introduce an artificial source during the calibration steps as it has no phase errors imprinted. For this example, we now re-introduce Sgr A*. Once more, we start by copying the combined datasets:  


<pre style="background-color: lightgrey;”>
<pre style="background-color: lightgrey;”>
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In Fig. 9 we show the resulting images on the same scales as in Fig. 8a-b. As expected the  main difference is the added point source but some differences due to the [https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.imaging.tclean.html tclean] algorithm are also visible. Again, one can improve the images as described above.
In Fig. 9 we show the resulting images on the same scales as in Fig. 8a-b. As expected the  main difference is the added point source but some differences due to the [https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.imaging.tclean.html tclean] algorithm are also visible. Again, one can improve the images as described above.


== Tips for Selfcal ==
If the source has a bright nucleus or, more generally, a bright unresolved emission, start with the A array data, selfcal, then add B array, selfcal again, and so on. This procedure starts with a high model flux that is increased further. 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.
In our case the steps would be done in the images which have Sgr A* removed; otherwise the variability of Sgr A* prevents such procedures. Also, when we replaced Sgr A* by an artificial 1 Jy source, that source will not have phase fluctuations imprinted and cannot be used for selfcal. It can be reintroduced as described above after all selfcal steps are completed.


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Edited: Updated to CASA 5.4.0 version, Angelica Vargas October 2018
Edited: Updated to CASA 5.4.0 version, Angelica Vargas October 2018
Edited: Updated to CASA 5.5.0, Tony Perreault May 2019
Edited: Updated to CASA 5.5.0, Tony Perreault May 2019
Edited: Split off from VLA Data Combination guide to form new guide, Trent Seelig 2021-10-14
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{{Checked 6.2.0}}
{{Checked 6.2.0}}

Revision as of 22:24, 14 October 2021

This tutorial was created and tested using CASA 6.2.0

Introduction

Occasionally, observers may find that the emission of one source overlaps with that of nearby sources. This overlap can present analytical challenges if the nearby sources, which are the targets of interest, become obscured. These problems may arise when observing crowded fields or very bright objects. By modelling the emission of the obscuring source, or sources, one can then subtract it from the dataset and thus disentangle the emission.

In this tutorial, we will subtract the variable and unresolved point source of Sgr A*, the central supermassive black hole of our Milky Way from a dataset that was obtained in a monitoring campaign. This dataset includes observations from three different configurations of the VLA. To understand how to combine these three datasets into a single dataset the user is advised to study our guide on data combination. After performing this combination the user may then refer to the final section where we will show how to insert back into the dataset a model of the subtracted source.

The Data

We will be removing the point source from three different S-band datasets obtained in the A, B, and C configurations, from the NRAO Monitoring of the Galactic Center/G2 Cloud Encounter. The datasets have all been calibrated and had their science targets 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 download the file above, then untar: 

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

This will unpack three MeasurementSets (MSs), 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 on-source integration time, resolution, and rms.

Start CASA: 

# In a Terminal
casa

As a first step, let's have a look at the 'listobs' output for the different MSs. 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       4701.6Difference between revisions of "VLA Data Combination-CASA6.2.0"
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Revision as of 17:03, 9 August 2021 (edit)
Jott (talk | contribs)
(→‎Removal of the variable Sgr A* point source)
← Older edit
	
Latest revision as of 17:18, 9 August 2021 (edit) (undo)
Jott (talk | contribs)
(→‎Removal of the variable Sgr A* point source)
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##### End Task: listobs              #####
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We see that the target source for A-configuration is J1745-2900 (Sgr A*) (the target source has been split out to remove all calibrators). The on-source integration time amounts to only six minutes and the 14 spectral windows span a frequency range from 1.988 to 4.012GHz. Inspection of the other array configuration files show almost identical setups.

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

# In CASA
# A-config: 
plotms(vis='VLA-SgrA-Sband-Aconfig.ms',xaxis='Uwave',yaxis='Vwave')

# In CASA
# B-config
plotms(vis='VLA-SgrA-Sband-Bconfig.ms',xaxis='Uwave',yaxis='Vwave')

# In CASA
# C-config:
plotms(vis='VLA-SgrA-Sband-Cconfig.ms',xaxis='Uwave',yaxis='Vwave')

The uv-coverage plots are shown in Fig. 1a-c. 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, as expected, between the VLA A, B, and C configurations.


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

The next step is to create images we will then model and subtract the point source from.

To keep things simple, we will use common cell sizes of 0.1 arcsec, which samples well the A-configuration beam and oversamples the more compact configurations. We create images of 1280 pixels on each side, and we will tclean 1000 iterations (see the VLA CASA imaging guide for more sophisticated imaging parameter choices). : 

# In CASA
# A-config: 
tclean(vis='VLA-SgrA-Sband-Aconfig.ms', imagename='SgrA-Aonly',field='J1745-2900',
      specmode='mfs',cell='0.1arcsec',imsize=[1280,1280],niter=1000,weighting='briggs',robust=0)

# In CASA
# B-config
tclean(vis='VLA-SgrA-Sband-Bconfig.ms', imagename='SgrA-Bonly',field='J1745-2900',
      specmode='mfs',cell='0.1arcsec',imsize=[1280,1280],niter=1000,weighting='briggs',robust=0)

# In CASA
# C-config:
tclean(vis='VLA-SgrA-Sband-Cconfig.ms', imagename='SgrA-Conly',field='J1745-2900',
      specmode='mfs',cell='0.1arcsec',imsize=[1280,1280],niter=1000,weighting='briggs',robust=0)

The clean images, shown in Fig. 2a-c, give a first impression of the data.

Figure 2a:
Simple image of the A-configuration data. To get a similar image, in the Viewer task, click on the Wrench icon and in the Basic setting change the data range to [-0.01, 0.03]
Figure 2b:
Simple image of the B-configuration data. To get a similar image, in the Viewer task, click on the Wrench icon and in the Basic setting change the data range to [-0.01, 0.115]
Figure 2c:
Simple image of the C-configuration data. To get a similar image, in the Viewer task, click on the Wrench icon and in the Basic setting change the data range to [-0.1, 1]

The basic parameters of the individual images can be checked through imhead for the spatial resolution and determining the rms when checking the statistics of empty areas within the viewer. 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 (get the peak flux values from within the viewer or use the imstat task): 

A-configuration: synthesized beam  1.40" x 0.40"; rms ~0.8mJy; peak flux density of Sgr A* 0.713 Jy/beam
B-configuration: synthesized beam  4.12" x 1.31"; rms ~0.7mJy; peak flux density of Sgr A* 0.691 Jy/beam
C-configuration: synthesized beam 11.18" x 3.98"; rms ~0.6mJy; peak flux density of Sgr A* 1.314 Jy/beam

Removal of the variable Sgr A* point source

We have seen in the initial images that Sgr A* is a variable source. The variability would introduces problems if a user wished to combine the datasets as the visibilities of the different sessions will reflect these variations. Cleaning would be difficult in such a situation as flux levels at different uv points (times and baselines) would not be consistent in their portion for the central point source. Therefore, we will remove the unresolved Sgr A* in each dataset. For those who are interested in having Sgr A* back in the image, we will later show how to re-introduce a nominal point source with a flux of 1Jy. It is perfectly fine, however, to image without the artificial source.

To start, we create a component list for each dataset that includes only a point source at the position of Sgr A*. To do so, we first need to find out the position of the central point source. This is performed by fitting a point source (2d-Gaussian function) to the A-configuration data, which has the largest angular resolution. We use imfit: 

# In CASA
imfit(imagename='SgrA-Aonly.image',box='630,620,650,660')

The output of imfit into the logger is as follows: 


Fit on SgrA-Aonly.image component 0
Position ---
       --- ra:    17:45:40.038545 +/- 0.000057 s (0.000748 arcsec along great circle)
       --- dec: -029.00.28.051094 +/- 0.004412 arcsec
       --- ra:  639.9679 +/- 0.0075 pixels
       --- dec: 640.1790 +/- 0.0441 pixels

We will be using RA (J2000) =17h45m40.039s and DEC (J2000) = -29d00'28.05" for the central position of Sgr A*. We will use these coordinates for all three configurations, although it might be advisable to fit each dataset separately in case the peak may slightly shift. In this case, a single position works for all of our three data sets.

The component list will be inserted as a MODEL column into the respective MS via ft. Task uvsub will then be used to 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, then subtract. Note that running uvsub twice will oversubtract; we recommend to create backup copies of the previous datasets before subtracting Sgr A*: 

# In a Terminal
cp -r VLA-SgrA-Sband-Aconfig.ms VLA-SgrA-Sband-Aconfig-sub.ms
cp -r VLA-SgrA-Sband-Bconfig.ms VLA-SgrA-Sband-Bconfig-sub.ms
cp -r VLA-SgrA-Sband-Cconfig.ms VLA-SgrA-Sband-Cconfig-sub.ms


Let's start with the A-configuration data and create a component list of a point source with a flux of 0.712 Jy at a position of RA (J2000) =17h45m40.038547s and DEC (J2000) = -29d00'28.051226" (more examples of component lists are provided in the CASA guide Create a Component List for Selfcal): 

# In CASA
cl.addcomponent(flux=0.713, fluxunit='Jy',shape='point', dir='J2000 17:45:40.039 -29.00.28.05')
cl.rename('component-SgrA-A.cl')
cl.close()

(We have used the CASA coordinate convention with colons for h:m:s and dots for d.m.s.)

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

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

At this step the MODEL column is created.

Finally we will subtract the MODEL from the DATA/CORRECTED_DATA with uvsub. This task will also create CORRECTED as it does not exist yet: 

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

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

uvsub in CASA 6.2.0 has a bug and will show an error message like 'name 'vis' is not defined'. The task still appears to work correctly though, just the history entry seems to be compromised.


Let's repeat the steps for B-configuration, now using a flux of 0.691 Jy. Although a separate fit would be even better, the subtraction also works well when using the A-configuration position: 

# In CASA
cl.addcomponent(flux=0.691, fluxunit='Jy',shape='point', dir='J2000 17:45:40.039 -29.00.28.05')
cl.rename('component-SgrA-B.cl')
cl.close()
#
ft(vis='VLA-SgrA-Sband-Bconfig-sub.ms', complist='component-SgrA-B.cl', usescratch=True)
#
uvsub(vis='VLA-SgrA-Sband-Bconfig-sub.ms')

And for C-configuration we subtract a point source of 1.314 Jy (note that the central flux includes some extended emission, but is still dominated by Sgr A*): 

# In CASA
cl.addcomponent(flux=1.314, fluxunit='Jy',shape='point', dir='J2000 17:45:40.039 -29.00.28.05')
cl.rename('component-SgrA-C.cl')
cl.close()
#
ft(vis='VLA-SgrA-Sband-Cconfig-sub.ms', complist='component-SgrA-C.cl', usescratch=True)
#
uvsub(vis='VLA-SgrA-Sband-Cconfig-sub.ms')


Let's image the three data sets with Sgr A* removed. We will run tclean, but since the point source is removed, we only do 500 iterations for A and B configurations, and 200 iterations for the C configuration data to avoid cleaning into the noise: 

# In CASA
# A-config: 
tclean(vis='VLA-SgrA-Sband-Aconfig-sub.ms', imagename='SgrA-Aonly-noPNT',field='J1745-2900',
      specmode='mfs',cell='0.1arcsec',imsize=[1280,1280],niter=500,weighting='briggs',robust=0)

# In CASA
# B-config:
tclean(vis='VLA-SgrA-Sband-Bconfig-sub.ms', imagename='SgrA-Bonly-noPNT',field='J1745-2900',
      specmode='mfs',cell='0.1arcsec',imsize=[1280,1280],niter=500,weighting='briggs',robust=0)

# In CASA
# C-config:
tclean(vis='VLA-SgrA-Sband-Cconfig-sub.ms', imagename='SgrA-Conly-noPNT',field='J1745-2900',
      specmode='mfs',cell='0.1arcsec',imsize=[1280,1280],niter=200,weighting='briggs',robust=0)
Figure 3a:
Simple image of the A-configuration data with the central point source removed. To get a similar image, in the Viewer task, click on the Wrench icon and in the Basic setting change the data range to [-0.01, 0.03]
Figure 3b:
Simple image of the B-configuration data with the central point source removed. To get a similar image, in the Viewer task, click on the Wrench icon and in the Basic setting change the data range to [-0.01, 0.115]
Figure 3c:
Simple image of the C-configuration data with the central point source removed. To get a similar image, in the Viewer task, click on the Wrench icon and in the Basic setting change the data range to [-0.091, 0.618]

As expected, the images without the point sources as shown in Fig. 3a-c look similar to those in Fig. 2a-c. but with the central point source removed. Since we cleaned a given number of iterations, some differences are visible, particularly for C-configuration. This is expected.


Inserting an artificial Point Source for Sgr A*

Prior to this step, please refer to the CASA guide on multi configuration data combination as we will now be using a combined dataset of the three configurations

We can also add in a point source with a canonical flux density of 1 Jy to bring back Sgr A*. We again create a component list and use ft to attach the source model. Using uvsub with reverse=True will add instead of subtract the MODEL to DATA/CORRECTED. This step can be skipped when Sgr A* is not required. When the data are going to be self-calibrated, we actually recommend to not introduce an artificial source during the calibration steps as it has no phase errors imprinted. For this example, we now re-introduce Sgr A*. Once more, we start by copying the combined datasets: 

# In a Terminal
cp -r VLA-SgrA-Sband-statwt-combined.ms VLA-SgrA-Sband-statwt-combined-addPNT.ms

Now we create a component list with a 1 Jy point source at the previously derived position of Sgr A*. 

# In CASA
cl.addcomponent(flux=1, fluxunit='Jy',shape='point', dir='J2000 17:45:40.039 -29.00.28.05')
cl.rename('component-SgrA.cl')
cl.close()

Finally, we insert it in the model column with ft and add it to the data with uvsub to both MSs, the one with initialized weights and the one with recomputed weights: 

# In CASA
ft(vis='VLA-SgrA-Sband-statwt-combined-addPNT.ms', complist='component-SgrA.cl', usescratch=True)
#
uvsub(vis='VLA-SgrA-Sband-statwt-combined-addPNT.ms',reverse=True)

Now it is time to image both combined MSs once more: 


# In CASA
tclean(vis=['VLA-SgrA-Sband-statwt-combined-addPNT.ms'], 
      imagename='GC-statwt-all-addPNT',field='J1745-2900',specmode='mfs',cell='0.1arcsec',imsize=[1280,1280],niter=5000,
      threshold='5mJy',weighting='briggs',robust=0)
Figure 9:
Combined image with statwt and point source. To get a similar image, in the Viewer task, click on the Wrench icon and in the Basic setting change the data range to [0, 0.05]

In Fig. 9 we show the resulting images on the same scales as in Fig. 8a-b. As expected the main difference is the added point source but some differences due to the tclean algorithm are also visible. Again, one can improve the images as described above.


Last checked on CASA Version 6.2.0.

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