Difference between revisions of "MG0414+0534 P-band Spectral Line Tutorial - CASA 5.4.0"
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[[File:Pband_delaycal_5.4.0.png|200px|thumb|right|Figure 4. Delays for each antenna and each polarization. Plot made with CASA task [https://casa.nrao.edu/casadocs/casa-5
[[File:Pband_delaycal_5.4.0.png|200px|thumb|right|Figure 4. Delays for each antenna and each polarization. Plot made with CASA task [https://casa.nrao.edu/casadocs/casa-54.0/global-task-list//about ].]]
The calibration solutions can be plotted using CASA task [https://casa.nrao.edu/casadocs/casa-5-4.0/global-task-list/task_plotms/about plotms] (see Figure 4):
The calibration solutions can be plotted using CASA task [https://casa.nrao.edu/casadocs/casa-5-4.0/global-task-list/task_plotms/about plotms] (see Figure 4):
Revision as of 10:48, 12 October 2018
This CASA Guide is for Version 5.4.0 of CASA.
|The following guide is for users who are experts in data reduction using CASA. If you are a beginning or novice user, please review other CASA guides found at VLA Tutorials.|
- 1 Overview
- 2 Obtaining the data
- 3 Examining the Data
- 4 Calibration
- 5 Imaging and Self-calibration
This tutorial describes how to use CASA 5.4.0 to reduce spectral-line data in the low-frequency P-band of the VLA (230–470 MHz). The goal is to make an image cube containing HI 21cm absorption against the strong radio continuum of gravitationally lensed radio galaxy MG0414+0534. As a results of the high redshift of z=2.6365, the HI absorption signal in MG0414+0534 is redshifted to an observed frequency of 390.597 MHz. The HI absorption in MG0414+0534 was previously imaged with the VLA by Moore, Carilli & Menten 1999 (ApJ, 510, 87), before the upgrade to the WIDAR system.
To perform P-band spectrsocopy, there are three important considerations for planning the observations:
- Use a bandpass calibrator that is strong enough to accurately calibrate the frequency-dependent gain variations. This is particularly important for most HI 21cm absorption projects, which are typically performed against strong radio continuum sources. As a rule-of-thumb, use tcal > tobj × (Sobj / Scal)2, with t the exposure time and S the source flux density. This rule-of-thumb serves to avoid introducing excessive additional noise in the spectrum.
- Additionally, if very high spectral dynamic range is needed (i.e., when expecting a ratio between the detection limit and the radio continuum of about 1:10,000 or more), consider observing a bandpass calibrator several times during your run to be able to correct for time-varying bandpass changes, which scale with the continuum emission in the target field. See the Calibration section of the Guide to Observing with the VLA for more information.
- Use a bandwidth that is wide enough to perform accurate self calibration. Using a wide bandwidth for self-calibration is important for fields with relatively weak continuum sources. For strong continuum sources, a narrower bandwidth can be used to avoid excessive RFI.
The P-band test data on MG0414+0534 that we use in this tutorial were obtained using a large bandwidth. This is to ensure that good bandpass solutions can be procured, and that self-calibration can accurately be performed. However, due to the strong radio continuum of MG0414+0534 (3.3 Jy at 390 MHz), and the large amounts of RFI across the entire band, we only use a small fraction of the total band for data reduction and analysis in this tutorial.
Obtaining the data
This 15 GB data set can be downloaded by clicking here. The filename should be MG0414_d1_data.ms.tgz (add the ".tgz" if this is missing). We are providing this starting data set, rather than the original data set for two reasons. First, many of these initial processing steps can be rather time consuming (> 1 hr). Second, while necessary, many of these steps are not fundamental to the calibration and imaging process, which is the focus of this tutorial.
We will use test data that was taken in a hybrid configuration when the VLA array was moved from B-config to A-config. The data set can be downloaded from the NRAO archive by searching for the following Project Code:
This returns a long list with test-data that are publicly available. Our observations were performed on 14 Sept 2016:
Note that this observation was duplicated on 15 September 2016. For the purpose of this tutorial, we only reduce and image the first data set. The 15 GB data set from the download has already had online flagging and Hanning smoothing applied, and the smaller data set split out from the larger data set. If you wish, you can download the SDM from the NRAO archive in order to apply the online flags and run Hanning smoothing on the large data set before splitting out the smaller data set to work on.
Examining the Data
Loading data into CASA
We will have to untar the 15 GB data set that you've downloaded.
# in a terminal, outside of CASA: tar -xzf MG0414_d1_data.ms.tgz
Please use CASA 5.4.0 for this tutorial (typing casa -ls in a linux window shows the available versions and the current version; to explicitly change the version type, e.g., casa -r 5.4.0-68)
on the command line. This should start a CASA interactive python (iPython) session, and open a separate log window. To guarantee that the below mentioned procedure for data reduction and imaging works, make sure you are using CASA version 5.4.0. While older versions may work as well for the purpose of this tutorial, it is good to read instructions on how to download and install the latest version of CASA.
It is a good practice to read the Operator Log that was created by the duty operator during the observations. This log can provide additional information on data that should be flagged manually during the data reduction stage. The Operator logs can be found at Ops Logs form entry, and this is the link for the log of the observation (in PDF format).
Inspecting the observation info
We will inspect the content of our data set using CASA task listobs
# In CASA listobs(vis='MG0414_d1_data.ms', listfile='listobs.txt')
This normally plots an overview of the data in the CASA logger, but with the parameter listfile=`listobs.txt’, this information is written out to the file listobs.txt and is included below:
Observer: . Frazer Owen Project: uid://evla/pdb/1695465 Observation: EVLA Data records: 991814 Total elapsed time = 9390 seconds Observed from 14-Sep-2016/06:43:48.0 to 14-Sep-2016/09:20:18.0 (UTC) ObservationID = 0 ArrayID = 0 Date Timerange (UTC) Scan FldId FieldName nRows SpwIds Average Interval(s) ScanIntent 14-Sep-2016/06:43:48.0 - 06:48:42.0 7 0 3C48 33861   [CALIBRATE_AMPLI#UNSPECIFIED,CALIBRATE_BANDPASS#UNSPECIFIED,CALIBRATE_FLUX#UNSPECIFIED,CALIBRATE_PHASE#UNSPECIFIED] 06:56:21.0 - 07:08:39.0 8 1 MG0414+0534 82702   [OBSERVE_TARGET#UNSPECIFIED] 07:08:42.0 - 07:28:36.0 9 1 MG0414+0534 139698   [OBSERVE_TARGET#UNSPECIFIED] 07:28:39.0 - 07:48:33.0 10 1 MG0414+0534 139698   [OBSERVE_TARGET#UNSPECIFIED] 07:48:36.0 - 08:08:30.0 11 1 MG0414+0534 139698   [OBSERVE_TARGET#UNSPECIFIED] 08:10:30.0 - 08:14:30.0 12 0 3C48 26549   [CALIBRATE_AMPLI#UNSPECIFIED,CALIBRATE_BANDPASS#UNSPECIFIED,CALIBRATE_FLUX#UNSPECIFIED,CALIBRATE_PHASE#UNSPECIFIED] 08:16:30.0 - 08:34:27.0 13 1 MG0414+0534 124918   [OBSERVE_TARGET#UNSPECIFIED] 08:34:30.0 - 08:54:21.0 14 1 MG0414+0534 139347   [OBSERVE_TARGET#UNSPECIFIED] 08:54:24.0 - 09:14:18.0 15 1 MG0414+0534 139048   [OBSERVE_TARGET#UNSPECIFIED] 09:16:21.0 - 09:20:18.0 16 0 3C48 26295   [CALIBRATE_AMPLI#UNSPECIFIED,CALIBRATE_BANDPASS#UNSPECIFIED,CALIBRATE_FLUX#UNSPECIFIED,CALIBRATE_PHASE#UNSPECIFIED] (nRows = Total number of rows per scan) Fields: 2 ID Code Name RA Decl Epoch SrcId nRows 0 NONE 3C48 01:37:41.299431 +33.09.35.13299 J2000 0 86705 1 NONE MG0414+0534 04:14:37.800000 +05.34.41.99999 J2000 1 905109 Spectral Windows: (1 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_P#A0C0#17 466 TOPO 386.312 15.625 7281.2 389.9453 12 XX XY YX YY Sources: 2 ID Name SpwId RestFreq(MHz) SysVel(km/s) 0 3C48 0 - - 1 MG0414+0534 0 - - Antennas: 27: ID Name Station Diam. Long. Lat. Offset from array center (m) ITRF Geocentric coordinates (m) East North Elevation x y z 0 ea01 W24 25.0 m -220.127.116.11 +33.53.04.0 -2673.3457 -1784.6009 10.4844 -1604008.744400 -5042135.825100 3553403.710800 1 ea02 W56 25.0 m -18.104.22.168 +22.214.171.124 -11333.1991 -7637.6832 15.3636 -1613255.391400 -5042613.097800 3548545.906000 2 ea03 W40 25.0 m -126.96.36.199 +188.8.131.52 -6377.9680 -4286.7773 8.2312 -1607962.451800 -5042338.218100 3551324.962400 3 ea04 E04 25.0 m -107.37.00.8 +184.108.40.206 102.7973 -63.7800 -2.6177 -1601068.806000 -5042051.932700 3554824.838800 4 ea05 E36 25.0 m -220.127.116.11 +18.104.22.168 5761.3733 -2704.6731 33.0133 -1596127.730800 -5045193.742100 3552652.419700 5 ea06 N24 25.0 m -22.214.171.124 +126.96.36.199 -290.3584 2961.8653 -12.2425 -1600930.069900 -5040316.395500 3557330.390000 6 ea07 N16 25.0 m -188.8.131.52 +184.108.40.206 -155.8512 1426.6377 -9.3934 -1601061.954400 -5041175.875300 3556058.026700 7 ea08 W64 25.0 m -220.127.116.11 +18.104.22.168 -14240.7524 -9606.2900 17.0885 -1616361.575500 -5042770.516600 3546911.419900 8 ea09 N40 25.0 m -22.214.171.124 +126.96.36.199 -633.6056 6878.5897 -20.7984 -1600592.749000 -5038121.341300 3560574.826900 9 ea10 E20 25.0 m -188.8.131.52 +184.108.40.206 2082.1759 -987.0087 9.4361 -1599340.800100 -5043150.963000 3554065.231500 10 ea12 N32 25.0 m -220.127.116.11 +18.104.22.168 -441.7248 4689.9674 -16.9328 -1600781.044500 -5039347.439100 3558761.527100 11 ea13 E28 25.0 m -22.214.171.124 +33.53.04.9 3732.7776 -1757.3308 21.4271 -1597899.895900 -5044068.684700 3553432.450200 12 ea14 E08 25.0 m -126.96.36.199 +188.8.131.52 407.8283 -206.0315 -3.2236 -1600801.931400 -5042219.382600 3554706.429400 13 ea15 N64 25.0 m -184.108.40.206 +34.02.20.5 -1382.3871 15410.1326 -40.6450 -1599855.687000 -5033332.368600 3567636.606800 14 ea16 W72 25.0 m -220.127.116.11 +18.104.22.168 -17419.4641 -11760.2694 14.9442 -1619757.299900 -5042937.656400 3545120.392300 15 ea17 N56 25.0 m -22.214.171.124 +34.00.38.4 -1105.2042 12254.2800 -34.2710 -1600128.378100 -5035104.139200 3565024.633900 16 ea18 W48 25.0 m -126.96.36.199 +188.8.131.52 -8707.9181 -5861.7682 15.5302 -1610451.901900 -5042471.123800 3550021.073100 17 ea19 W16 25.0 m -184.108.40.206 +220.127.116.11 -1348.7121 -890.6209 1.2959 -1602592.853500 -5042054.992400 3554140.702800 18 ea20 E16 25.0 m -107.36.09.8 +18.104.22.168 1410.0403 -673.4656 -0.7790 -1599926.104100 -5042772.977200 3554319.801100 19 ea21 E24 25.0 m -22.214.171.124 +126.96.36.199 2858.1906 -1349.1352 13.7186 -1598663.082000 -5043581.391200 3553767.014100 20 ea22 W32 25.0 m -188.8.131.52 +184.108.40.206 -4359.4399 -2923.1314 11.7614 -1605808.634100 -5042230.084000 3552459.197800 21 ea23 E12 25.0 m -220.127.116.11 +18.104.22.168 848.6994 -411.6226 -2.7575 -1600416.518000 -5042462.430500 3554536.041700 22 ea24 W08 25.0 m -22.214.171.124 +126.96.36.199 -432.1080 -272.1514 -1.5061 -1601614.083200 -5042001.656900 3554652.505900 23 ea25 N08 25.0 m -107.37.07.5 +188.8.131.52 -68.9088 433.1829 -5.0674 -1601147.942500 -5041733.833600 3555235.947000 24 ea26 E32 25.0 m -107.34.01.5 +184.108.40.206 4701.6503 -2209.7119 25.1976 -1597053.124400 -5044604.675000 3553058.992700 25 ea27 N48 25.0 m -220.127.116.11 +33.59.06.2 -855.2719 9405.9407 -25.9485 -1600374.881000 -5036704.201700 3562667.858900 26 ea28 N72 25.0 m -18.104.22.168 +34.04.12.2 -1685.6797 18861.8306 -43.5015 -1599557.928700 -5031396.353400 3570494.736800
|Figure 1a. Plotants plot for
|Figure 1b. Plotants plot |
for MG0414 zoomed in.
Next we look at a graphical plot of the antenna locations and save a hard copy in case you want it later using CASA task plotants (see Figures 1a and 1b). This will be useful for selecting a reference antenna. Typically a good choice is an antenna close to the center of the array (see Figure 1b). Unless it shows problems after inspection of the data, we provisionally choose ea04. (Note: clicking on any image will open up a larger version.)
# In CASA plotants(vis='MG0414_d1_data.ms',figfile='ant_locations.png')
We will now perform a more in-depth flagging of the bad data. Our strategy is to first focus on the calibrator source (in this case only 3C48). If the calibrator data are flagged well, we should be able to perform an accurate calibration of the target data. Later in this document we will then flag the calibrated target data using an automated flagger.
When plotting frequency against amplitude in CASA task plotms, we see that there are still two ranges of channels filled with RFI throughout the observing run (see Figure 2).
# In CASA plotms(vis='MG0414_d1_data.ms',antenna='ea01',xaxis='chan',yaxis='amp',correlation='xx,yy',coloraxis='baseline', scan='7',averagedata=True, avgtime='1e9')
We will flag this data with the CASA task flagdata. Because we are flagging channels on all scans of the bandpass calibrator 3C48, these channels cannot be calibrated accurately, so we flag these channels also in the data of the target.
# In CASA flagdata(vis='MG0414_d1_data.ms', mode='manual', spw='0:148~155', action='apply') flagdata(vis='MG0414_d1_data.ms', mode='manual', spw='0:51~59', action='apply')
The data on our bandpass+gain+flux calibrator 3C48 now look clean, with no dead or misbehaving antennas (plotted using the same plotms call as above). We now start with the calibration.
Many of the steps below are based on the CASA tutorial on reducing VLA P-band continuum data.
A priori Antenna Position Corrections
We start the calibration by obtaining the latest set of antenna-position corrections compared to the stored values that were derived at the start of the observing period. For this you need to run CASA task gencal with parameter caltype='antpos'. (Be sure to have internet connection in order to grab the antenna position corrections.)
# In CASA gencal(vis='MG0414_d1_data.ms', caltype='antpos', caltable='antpos.cal')
The output is a list of antenna position corrections that are written to the calibration table antpos.cal:
2017-07-25 19:54:51 INFO gencal Determine antenna position offests from the baseline correction database 2017-07-25 19:54:52 INFO gencal offsets for antenna ea01 : 0.00200 0.00000 0.00200 2017-07-25 19:54:52 INFO gencal offsets for antenna ea02 : 0.00210 0.01800 0.00430 2017-07-25 19:54:52 INFO gencal offsets for antenna ea03 : -0.00700 0.01600 -0.02000 2017-07-25 19:54:52 INFO gencal offsets for antenna ea06 : 0.00200 -0.00200 0.00500 2017-07-25 19:54:52 INFO gencal offsets for antenna ea08 : -0.01290 0.01280 0.02920 2017-07-25 19:54:52 INFO gencal offsets for antenna ea09 : -0.01200 -0.01300 0.02400 2017-07-25 19:54:52 INFO gencal offsets for antenna ea15 : -0.00500 -0.01000 0.01100 2017-07-25 19:54:52 INFO gencal offsets for antenna ea16 : 0.01100 -0.01500 -0.01200 2017-07-25 19:54:52 INFO gencal offsets for antenna ea17 : -0.01300 0.00000 0.05000 2017-07-25 19:54:52 INFO gencal offsets for antenna ea18 : -0.00600 0.02200 -0.02000 2017-07-25 19:54:52 INFO gencal offsets for antenna ea22 : 0.00600 0.00000 0.00000 2017-07-25 19:54:52 INFO gencal offsets for antenna ea25 : -0.00140 0.00140 -0.00140 2017-07-25 19:54:52 INFO gencal offsets for antenna ea27 : -0.00400 -0.00500 0.03100 2017-07-25 19:54:52 INFO calibrater Beginning specifycal----------------------- 2017-07-25 19:54:52 INFO Creating KAntPos Jones table from specified parameters. 2017-07-25 19:54:52 WARN NB: This EVLA dataset appears to fall within the period 2017-07-25 19:54:52 WARN + of semester 16B during which the online tropospheric 2017-07-25 19:54:52 WARN + delay model was mis-applied. 2017-07-25 19:54:52 WARN A correction for the online tropospheric delay model error WILL BE APPLIED! 2017-07-25 19:54:52 WARN Marking antpos caltable to turn ON the trop delay correction.
Note that these observations were taken during a period in which the atmospheric delay terms were calculated incorrectly. In CASA versions 4.7.1 and up a correction for this term is taken into account automatically when running CASA task gencal with parameter caltype='antpos'.
At frequencies below 5 GHz, it is important to correct for ionospheric effects. CASA’s strategy is to obtain information on the total electron content (TEC) for the date of observations, which is based on data from the global navigation satellite system (GNSS). A series of CASA images in the form of a 24 hour movie of the TEC, as function of longitude and latitude, is then generated and stored as filename.IGS_TEC.im, with a corresponding TEC error movie named filename.IGS_RMS_TEC.im (these movies can be viewed in CASA’s viewer). In order to download the data, you need to be connected to the internet:
# In CASA from recipes import tec_maps tec_image, tec_rms_image, plotname = tec_maps.create(vis='MG0414_d1_data.ms', doplot=True)
(If, for any reason, the file cannot be retrieved from the server automatically, you can also download this specific TEC file for the tutorial here).
The information regarding the TEC is sparse and either active or direction-dependent ionospheric conditions may not be corrected very well. Also, the online TEC information improves with time, hence the quality of these data is best about two weeks after the observations.
An ionosphere correction table is subsequently generated using CASA task gencal, in which the projected line-of-sight TEC (which depends on the zenith angle) is sampled for all times in the observation and stored in a standard CASA caltable. Figure 3 shows a plot that is generated for this caltable.
# In CASA gencal(vis='MG0414_d1_data.ms', caltype='tecim', caltable='tecim.cal',infile=tec_image)
See the CASA Cookbook for a detailed description on ionosphere corrections in CASA (thanks are due to Jason Kooi, University of Iowa, for his contributions).
Calibration of requantizer gains
Next we will calibrate the requantizer gain levels, which are the visibility amplitudes that were set as the input of the WIDAR correlator. Requantizer scans are added to a P-band scheduling block (SB) to optimize the digital power in each spectral window which, in turn, maximizes the signal-to-noise of each window. Because there is a significant variation in power across the 240 MHz of P-band, and because some spectral windows may experience a high power due to strong RFI, setting the requantizer levels during the observations may improve the quality of the data, including the shape of the bandpass across multiple spectral windows. As part of the test observations of MG0414+0534, the very strong source Cygnus-A was observed at the start of the run. Re-setting the requantizer levels both before observing Cygnus-A and before targeting the other (much weaker) sources was essential to optimize the digital power and avoid correlation errors. Although we do not use Cygnus-A as part of this tutorial, and we use only a single spectral window, performing calibration of the requantizer gains is still good practice for P-band spectral line reduction using CASA task gencal
# In CASA gencal(vis='MG0414_d1_data.ms', caltype='rq', caltable='rq.cal')
Initial absolute flux density calibration
# In CASA setjy(vis='MG0414_d1_data.ms', field='0', scalebychan=True, standard='Perley-Butler 2017', listmodels=False, usescratch=False)
It is crucial for spectral line work to set the parameter scalebychan=True to ensure that the absolute flux level is calculated per channel and correctly interpolated across the observing band. If scalebychan=False, then only a single value per spectral window is calculated, resulting in a step function in flux between spectral windows.
The delay of each antenna, for each polarization and each spectral window, is now determined. Doing this on a short scan of the primary calibrator is generally sufficient and will correct for both internal (e.g., electronics, cables) and external (e.g., ionosphere) effects. We select ea04 as reference antenna as it produced good data, has baselines to all other used antennas in the array, and is located in the center of the array. We also need to apply, "on-the-fly," the calibration tables for the antenna positions, the total electron content, and the requantizer levels using the CASA task gaincal.
# In CASA gaincal(vis='MG0414_d1_data.ms', caltable='delays.cal', field='0', selectdata=True, timerange='06:43:48~06:48:42', solint='inf', refant='ea04', gaintype='K', gaintable=['antpos.cal','tecim.cal','rq.cal'])
The calibration solutions can be plotted using CASA task plotms (see Figure 4):
# In CASA plotms(vis='delays.cal',xaxis='antenna1',yaxis='delay',field='0',coloraxis='corr')
Note that any delays deviating by more than 30 nsec should be treated with caution and, if needed, flagged.
Next step is to calibrate the bandpass, which corrects for the frequency dependent gain variations. Before determining the frequency dependent gains, we first calibrate the phases in time, deriving a solution for each integration, to make sure that we can derive accurate bandpass solutions when integrating over a full scan on the bandpass calibrator 3C48. This is done using CASA task gaincal:
# In CASA gaincal(vis='MG0414_d1_data.ms', caltable='bpphase.gcal', field='0', spw='0:250~300', refant='ea04', calmode='p', solint='int', minsnr=3.0, gaintype='G', gaintable=['antpos.cal','tecim.cal','rq.cal','delays.cal'])
We use these phase solutions to perform the actual bandpass calibration. In this case, we have scans of our bandpass calibrator 3C48 taken at three different times during the observation. By interpolating the bandpass solutions in time, we try to minimize any possible time-varying bandpass effects and improve our spectral dynamic range. Bandpass calibration is performed in CASA by the task bandpass:
# In CASA bandpass(vis='MG0414_d1_data.ms', caltable='bandpass.cal', field='0', spw='0', solint='inf', combine='', refant='ea04', solnorm=False, bandtype='B', gaintable=['antpos.cal','tecim.cal','rq.cal','delays.cal','bpphase.gcal'])
To interpolate between bandpass scans, it is essential to specify parameter combine=' '. The reason being that the default of combine='scan', when coupled with the parameter solint='inf', would cross the scan boundaries to form one single solution for the bandpass. Only when combining solint='inf' with combine=' ' is a bandpass solution obtained for each individual scan on 3C48. The bandpass plots can be viewed using CASA task plotms (see Figure 5).
# In CASA plotms(vis='MG0414_d1_data.ms',field='0',antenna='ea04', xaxis='frequency',yaxis='amp',scan='7',correlation='xx,yy', coloraxis='baseline') plotms(vis='MG0414_d1_data.ms',field='0',antenna='ea04', xaxis='frequency',yaxis='phase',scan='7',correlation='xx,yy', coloraxis='baseline')
After the bandpass calibration, we perform a phase calibration of our data. Because we apply the bandpass solutions "on-the-fly," we can use the full bandwidth to determine the phase solutions. We will create two different calibration tables to correct for the time-varying phases. First we calibrate the phases on each interval with the CASA task gaincal parameter of solint set to int:
# In CASA gaincal(vis='MG0414_d1_data.ms', caltable='intphase.gcal', field='0', spw='0', refant='ea04', calmode='p', solint='int', minsnr=3.0, gaintable=['antpos.cal','tecim.cal','rq.cal','delays.cal','bandpass.cal'])
The sole purpose of generating phase solutions on each 10 second interval is to be able to accurately correct for the time-varying amplitudes as we will see below. This method, however, is not ideal for interpolating the phase solutions in time. For that we do a second phase calibration, this time averaging the signal across the phase calibrator scans with the CASA task gaincal parameter of solint='inf':
# In CASA gaincal(vis='MG0414_d1_data.ms', caltable='scanphase.gcal', field='0', spw='0', refant='ea04', calmode='p', solint='inf', combine='', minsnr=3.0, gaintable=['antpos.cal','tecim.cal','rq.cal','delays.cal','bandpass.cal'])
Note that an alternative approach to deriving phase solutions per scan is to use the CASA task smoothcal to average the phase corrections obtained every 10s with solin='int' over the full duration of a single scan. Generate a phase plot using CASA task plotms with the parameters below (see Figure 6):
# In CASA plotms(vis='MG0414_d1_data.ms',field='0',antenna='ea04', correlation='xx,yy',avgchannel='1e8', xaxis='time',yaxis='phase',coloraxis='baseline')
Scaling the Amplitude Gains
We now perform a calibration of the amplitude variations in time. For this, we will apply the phase solution obtained in each 10 second interval via CASA task gaincal:
# In CASA gaincal(vis='MG0414_d1_data.ms', caltable='amp.gcal', field='0', spw='0', selectdata=False, solint='inf', combine='', refant='ea04', calmode='ap', minsnr=3.0, gaintable=['antpos.cal','tecim.cal','rq.cal','delays.cal','bandpass.cal','intphase.gcal'])
Normally this procedure will only calibrate the relative amplitudes, and the CASA task fluxscale is required to place the data on an absolute flux scale. However, because our flux-calibrator is also our bandpass and gain calibrator, the absolute flux scale will already be ok, so we can skip this step. Generate an amplitude gain plot using CASA task plotms (see Figure 7) with the parameters below:
# In CASA plotms(vis='MG0414_d1_data.ms',field='0',antenna='ea04', correlation='xx,yy',avgchannel='1e8', xaxis='time',yaxis='amp',coloraxis='baseline')
Applying the calibration
We have now finished the calibration of the data. We will apply the calibration tables that were derived from 3C48 to both the calibrator 3C48 itself and our target MG0414+0354. The CASA task applycal creates a corrected data column where the calibrated data are stored.
# In CASA applycal(vis='MG0414_d1_data.ms', field='0', gaintable=['antpos.cal','tecim.cal','rq.cal','delays.cal','bandpass.cal','scanphase.gcal','amp.gcal'], gainfield=['','','','0','0','0','0'], parang=True, calwt=False, applymode='calflagstrict', flagbackup=True)
# In CASA applycal(vis='MG0414_d1_data.ms', field='1', gaintable=['antpos.cal','tecim.cal','rq.cal','delays.cal','bandpass.cal','scanphase.gcal','amp.gcal'], gainfield=['','','','0','0','0','0'], calwt=False, applymode='calflagstrict', flagbackup=True)
Sometimes after calibrating the data it is easier to see any offending antennas or baselines. Replotting with ydatacolumn='corrected' will produce the corrected calibration plots (Figures 8a, 8b).
# In CASA for corrected amplitude and phase vs frequency figure 8a plotms(vis='MG0414_d1_data.ms',field='0',antenna='ea04', xaxis='frequency',yaxis='amp',scan='7',ydatacolumn='corrected',correlation='xx,yy', coloraxis='baseline') plotms(vis='MG0414_d1_data.ms',field='0',antenna='ea04', xaxis='frequency',yaxis='phase',scan='7',ydatacolumn='corrected',correlation='xx,yy', coloraxis='baseline')
# In CASA for corrected phase vs time figure 8b plotms(vis='MG0414_d1_data.ms',field='0',antenna='ea04', correlation='xx,yy',avgchannel='1e8', xaxis='time',yaxis='phase',ydatacolumn='corrected',coloraxis='baseline')
|Figure 8a. Effects of bandpass
calibration on the frequency
dependent amplitudes (top) and
phases (bottom) across the
observing band. The plots were
made in CASA task plotms and
in the Axes tab choosing option
Data Column: corrected.
|Figure 8b. Effects of phase calibration.|
The various colors show the different
baselines with reference antenna
ea04. The plot was made in CASA task
plotms and in the Axes tab choosing
option Data Column: corrected.
The calibrated data of the primary calibrator 3C48 show that the YY amplitudes of ea17 are off during the second part of the run. Because calibration solutions are antenna based, to get the best calibration, flag any poor data and re-run the calibration process. A currently known issue is that CASA task gaincal will not find good solutions if any correlation, including cross-correlation, in the data is completely flagged. In this case, there is no difference between flagging only the YY polarization of ea17 or the entire antenna. Antenna ea17 will be flagged using CASA task flagdata:
# In CASA flagdata(vis='MG0414_d1_data.ms', mode='manual', antenna='ea17', action='apply')
Then run the calibration process again starting with the section on Delay calibration. Once you've re-run the calibration, plot the corrected time vs amplitudes with ea17 flagged (see Figure 9):
|Figure 9. Effects of amplitude and |
flux calibration. The green points
(the YY products of antenna ea17)
have been flagged out. The plot was
made in CASA task plotms and in
the Axes tab choosing option
Data Column: corrected.
# In CASA for corrected amp vs time with ea17 flagged out figure 9 plotms(vis='MG0414_d1_data.ms',field='0',antenna='ea04', correlation='xx,yy',avgchannel='1e8', xaxis='time',yaxis='amp',ydatacolumn='corrected',coloraxis='baseline')
Having re-run the calibration steps with ea17 flagged, we now split the calibrated data of our target into a new measurement set, including only the XX and YY polarization products. Using CASA task split the calibrated target data are placed into the data column of the new measurement set:
# In CASA split(vis='MG0414_d1_data.ms', outputvis='MG0414_d1_calibrated.ms', datacolumn='corrected', field='1', correlation='xx,yy', keepflags=False)
Imaging and Self-calibration
We now have a calibrated data set that has all calibration applied except for possible self-calibration. Self-calibration relies on the target itself to better calibrate antenna-based gains (phases and amplitudes) as a function of time. It can be applied to targets that are strong in the continuum or have a strong enough spectral line to derive good phase and, if desired, also amplitude solutions on short (order of minutes) timescales. The procedure relies on an initial model of the target, which you then use iteratively to improve the gains and hence the quality of the image itself (see Figure 10). Here we show the various steps in self-calibrating our data based on the strong continuum emission of MG0414+0354. We refer to a dedicated CASA guide on self-calibration for further details.
If strong RFI is present in the target data, it may be necessary to flag your data before proceeding with self-calibration. This is not the case with MG0414+0354.
We create an initial model of our target field using CASA task tclean:
# In CASA tclean(vis='MG0414_d1_calibrated.ms', datacolumn='data', imagename='MG0414_d1_cont_R03', spw='0', specmode='mfs', niter=100, threshold='1.5Jy', imsize=[256,256], weighting='briggs', robust=0.3, savemodel='modelcolumn')
The information in the MODEL column consist of visibilities that present a model of the target field. CASA task tclean produces a .model image of the model that you can visualize with viewer or CASA task plotms. It is essential that only components that you know for sure represent the continuum in the target field are taken into account in self-calibration. If this is not the case, you probably cleaned too deep and included noise features or artifacts (e.g., from the beam-pattern) in the model. Self-calibration would permanently lock these as features in your data, so this should be avoided. On the other hand, it is equally crucial to ensure that the model is not empty, as this will result in your target being shifted to the phase center.
Next, we derive phase corrections to this model. Because our target is strong enough, we should get enough S/N per baseline on a 60 sec interval, using CASA task gaincal:
# In CASA gaincal(vis='MG0414_d1_calibrated.ms', caltable='sc1.gcal', field='0', spw='0', refant='ea04', calmode='p', solint='60s', combine='', minsnr=3.0)
This creates a new phase-calibration table that we call sc1.gcal. We now apply this calibration table to the calibrated data in order to improve the phase-only solutions using CASA task applycal:
# In CASA applycal(vis='MG0414_d1_calibrated.ms', field='0', gaintable=['sc1.gcal'], interp='linear')
Because self-calibration is an iterative process, we want to repeat this step again in order to further improve the gain calibration. For that, we first split the calibrated data with the self-calibration solution applied to the DATA column of a new measurement set, again using CASA task split:
# In CASA split(vis='MG0414_d1_calibrated.ms', outputvis='MG0414_d1_sc1.ms', datacolumn='corrected', field='0', keepflags=False)
Now we can use this newly created measurement set to make a better continuum image and subsequently perform the next round of self-calibration:
# In CASA tclean(vis='MG0414_d1_sc1.ms', datacolumn='data', imagename='MG0414_d1_cont_sc1.R03', spw='0', specmode='mfs', niter=500, threshold='500mJy', imsize=[256,256],weighting='briggs',robust=0.3,savemodel='modelcolumn')
# In CASA gaincal(vis='MG0414_d1_sc1.ms', caltable='sc2.gcal', field='0', spw='0', refant='ea04', calmode='p', solint='30s', combine='', minsnr=3.0)
# In CASA applycal(vis='MG0414_d1_sc1.ms', field='0', gaintable=['sc2.gcal'], interp='linear')
# In CASA split(vis='MG0414_d1_sc1.ms', outputvis='MG0414_d1_sc2.ms', datacolumn='corrected', field='0', keepflags=False)
# In CASA tclean(vis='MG0414_d1_sc2.ms', datacolumn='data', imagename='MG0414_d1_cont_sc2_R03', spw='0', specmode='mfs', niter=1000, threshold='100mJy', imsize=[256,256],weighting='briggs',robust=0.3,savemodel='modelcolumn')
Now that we have a good model of the continuum in the field of MG0414+0354, we will attempt a final self-calibration using both the phases and amplitudes via CASA task gaincal :
# In CASA gaincal(vis='MG0414_d1_sc2.ms', caltable='sc3ap.gcal', field='0', spw='0', refant='ea04', calmode='ap', solint='30s', combine='', minsnr=3.0)
# In CASA applycal(vis='MG0414_d1_sc2.ms', field='0', gaintable=['sc3ap.gcal'], interp='linear')
# In CASA split(vis='MG0414_d1_sc2.ms', outputvis='MG0414_d1_sc3ap.ms', datacolumn='corrected', field='0', keepflags=False)
# In CASA tclean(vis='MG0414_d1_sc3ap.ms', datacolumn='data', imagename='MG0414_d1_cont_sc3ap_R03', spw='0', specmode='mfs', niter=1000, threshold='100mJy', imsize=[256,256],weighting='briggs',robust=0.3,savemodel='modelcolumn')
The output continuum image from the last iteration of CASA task tclean continues to be an improvement over previous imaging, but also shows that we are not likely to gain further improvement with additional rounds of self-calibration. This indicates that we now have a calibrated data-set of MG0414+0354 that is almost ready for final imaging.
However, before imaging, we need to flag the target data to remove poor-quality data. First, we run an automated flagging routine using CASA task flagdata with the parameter mode='rflag':
# In CASA flagdata(vis='MG0414_d1_sc3ap.ms', mode='rflag', datacolumn='data')
After having ran this automated flagging, we inspect the data via the CASA tasks viewer or plotms by displaying time vs. channel for the different baselines, or you may display time vs. baseline for the different channels. Keep in mind that when the data are properly calibrated, it is easier to recognize low-level imperfections which may have to be flagged to improve the image quality. In this case, viewer or plotms show that antennas ea09 and ea14 have significantly higher noise than the other antennas, something which was not picked up by the automated flagging routine. Additionally, for a number of baselines, the data quality looks relatively poor. We flag these antennas and baselines before imaging the data via the CASA flagdata task.
# In CASA flagdata(vis='MG0414_d1_data.ms', mode='manual', antenna='ea09', datacolumn='data') flagdata(vis='MG0414_d1_data.ms', mode='manual', antenna='ea14', datacolumn='data')
To flag offending individual baselines, in CASA task flagdata you may use antenna='antenna&antenna(&antenna...)'.
Flagging data can be an iterative process. Often, the better the data are calibrated, the easier it is to recognize low-level imperfections in the data. To optimize the data reduction, you can script the calibration process, and opt to flag the data also in the original data set (i.e., before calibration), after which you run the entire calibration again.
When you flag these antennas you must go back and rerun the calibration before the final imaging, starting at the Delay calibration section. If you do not re-run the calibration sequence and proceed ahead with the final tclean run then inspect the line-data cube (see below), you will get a plot that should look similar to Figure 11.
Finally, performing the calibration sequence again after flagging antennas ea09 and ea14, we image the data using the CASA task tclean:
# In CASA tclean(vis='MG0414_d1_sc3ap.ms', datacolumn='data', imagename='MG0414_d1_line_sc3ap_vel_R03', spw='0', specmode='cube', outframe='bary', veltype='optical', restfreq='390.600GHz', niter=100, threshold='1Jy', imsize=[256,256], pbcor=True, weighting='briggs', robust=0.3, savemodel='none')
We can inspect the resulting line-data cube in the viewer. The data cube contains both continuum and line emission, which can optionally be separated using the CASA task uvcontsub. The HI absorption feature is clearly present (see Figure 12).
To see your spectral line, open the last image of the source. Go to a channel that shows the source, then zoom in on the source. Click the Point marking icon, find the brightest pixel in the source and click on it. Now click on the spectral profile tool button to get the single point profile of the spectrum. Here you will see a spectrum with 2 dips that drop to zero; these dips are a part of the flagging we have done. You will need to zoom in in order to notice any spectral lines due to the auto scaling; zoom in by clicking on the graph and creating a yellow box then release.
Last checked on CASA Version 5.4.0.