Difference between revisions of "MG0414+0534 P-band Spectral Line Tutorial - CASA 5.0.0"

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To perform P-band spectrsocopy, there are three important considerations for planning the observations:
 
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 t<sub>cal</sub> > t<sub>obj</sub> × (S<sub>obj</sub> / S<sub>cal</sub>)<sup>2</sup>, with “t” the exposure time and “S” the source flux density. This requirement is to avoid introducing excessive additional noise in the spectrum. In addition, 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 https://science.nrao.edu/facilities/vla/docs/manuals/obsguide/calibration for more information.
+
* 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 t<sub>cal</sub> > t<sub>obj</sub> × (S<sub>obj</sub> / S<sub>cal</sub>)<sup>2</sup>, with “t” the exposure time and “S” the source flux density. This requirement is to avoid introducing excessive additional noise in the spectrum. In addition, 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 [https://science.nrao.edu/facilities/vla/docs/manuals/obsguide/calibration this page] for more information.
  
 
* Use a bandwidth that is wide enough to perform accurate self calibration. Using a wide bandwidth for self-calibration this is important for fields with relatively weak continuum sources. For strong continuum sources, a narrower bandwidth can be used to avoid excessive RFI.  
 
* Use a bandwidth that is wide enough to perform accurate self calibration. Using a wide bandwidth for self-calibration this is important for fields with relatively weak continuum sources. For strong continuum sources, a narrower bandwidth can be used to avoid excessive RFI.  
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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 [https://archive.nrao.edu/archive/advquery.jsp NRAO archive] by searching for the following Project Code:  
 
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 [https://archive.nrao.edu/archive/advquery.jsp NRAO archive] by searching for the following Project Code:  
  
 +
<div style="text-align:justify;">
 +
<pre style="background-color: #gray;">
 
TSUB0001
 
TSUB0001
 +
</pre>
 +
</div>
  
 
This returns a long list with test-data that are publicly available. Our observations were performed on 14 Sept 2016:
 
This returns a long list with test-data that are publicly available. Our observations were performed on 14 Sept 2016:
  
 +
<div style="text-align:justify;">
 +
<pre style="background-color: #gray;">
 
TSUB0001.sb32720781.eb32763188.57645.263958564814
 
TSUB0001.sb32720781.eb32763188.57645.263958564814
 +
</pre>
 +
</div>
  
 
Note that this observation was duplicated on 15 September 2016 . This second day of observations can be found under filename:
 
Note that this observation was duplicated on 15 September 2016 . This second day of observations can be found under filename:
Line 37: Line 45:
  
 
Click "Get My Data" will forward you to the next page, where you should choose the delivery method (either downloading over the internet, or sending home a hard-drive with the data). If you opt to retrieve the data over the internet, wait until you get an email confirming that the data is ready for download.
 
Click "Get My Data" will forward you to the next page, where you should choose the delivery method (either downloading over the internet, or sending home a hard-drive with the data). If you opt to retrieve the data over the internet, wait until you get an email confirming that the data is ready for download.
 +
 +
=== Loading data into CASA ===
 +
 +
Start CASA by typing
 +
<source lang='bash'>
 +
casa
 +
</source>
 +
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 5.0.0. While older version may work as well for the purpose of this tutorial, it is good to visit [https://casa.nrao.edu/casa_obtaining.shtml this page] for instruction on download and installation of the latest version of CASA.
 +
We will begin by importing our data from the binary format (SDM-BDF) into the MeasurementSet format, which is the standard for CASA data. For this, we use <i>importevla</i>:
 +
 +
<source lang='python'>
 +
# In CASA
 +
importevla(asdm='TSUB0001.sb32720781.eb32763188.57645.263958564814',vis='MG0414_d1.ms',flagpol=False,applyflags=False,savecmds=True,outfile='flagfile.txt')
 +
</source>
 +
 +
In this case, we do not apply the flags that were created as part of the observations, but we will write them out to a <i>flagfile.txt</i> file. We will inspect and apply the flags as follows:
 +
 +
<source lang='python'>
 +
# In CASA
 +
flagcmd(vis='MG0414_d1.ms',action='plot')
 +
flagcmd(vis='MG0414_d1.ms',action='apply')
 +
</source>
 +
 +
As an alternative to <i>flagcmd</i>, because we saved the flags to <i>flagfile.txt</i>, we can also use
 +
 +
<source lang='python'>
 +
# In CASA
 +
flagdata(vis='MG0414_d1.ms',mode='list',inpfile='flagfile.txt',action='apply')
 +
</source>
 +
 +
to apply the flags.
 +
 +
One of the flag commands is to <i>clip</i> all data points that have a 0-value, as these most likely have not recorded any science data. This is normally done with the settings <i>correlation=`ABS_RR’</i> and <i>correlation=`ABS_LL’</i>. Because the VLA P-band and 4-band systems use linear XX and YY polarisation, rather than the circular RR and LL polarisation of the other VLA bands, you will encounter an error message when running <i>flagcmd</i>. We can get around this issue by manually clipping the 0-data for the XX and LL polarization with <i>flagdata</i>:
 +
 +
<source lang='python'>
 +
# In CASA
 +
flagdata(vis='MG0414_d1.ms',mode='clip',correlation='ABS_XX,ABS_YY',clipzeros=True,action='apply')
 +
</source>
 +
 +
Note: It is good practice to also carefully read the Operator Log that was created by the operator on duty during the observations. This can provide additional information on data that should be flagged manually during the data reduction stage. These Operator logs can be found [http://www.vla.nrao.edu/cgi-bin/oplogs.cgi here].
 +
 +
Now that the data is read in and the first flagging is performed, we will inspect the content of our data set using <i>listobs</i>
 +
 +
<source lang='python'>
 +
# In CASA
 +
listobs(vis='MG0414_d1.ms',listfile='listobs.txt')
 +
</source>
 +
 +
This normally plots an overview of the data in the CASA logger, but with the commmand <i>listfile=`listobs.txt’</i> this information is written out to the file <i>listobs.txt</i>.

Revision as of 12:50, 18 August 2017


This CASA Guide is for Version 5.0.0 of CASA. THIS CASA GUIDE IS CURRENTLY IN PREPARATION (LAST ACTIVITY 18 AUG 2017)

Overview

This tutorial describes how to use CASA 5.0.0 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 GHz. 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.

Observing strategy

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 requirement is to avoid introducing excessive additional noise in the spectrum. In addition, 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 this page for more information.
  • Use a bandwidth that is wide enough to perform accurate self calibration. Using a wide bandwidth for self-calibration this 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 to ensure that good bandpass solutions could be obtained, and that self-calibration could accurately be performed. However, due to the strong radio continuum of MG0414+0534 (3.3 Jy at 390m 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 raw data

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:

TSUB0001

This returns a long list with test-data that are publicly available. Our observations were performed on 14 Sept 2016:

TSUB0001.sb32720781.eb32763188.57645.263958564814

Note that this observation was duplicated on 15 September 2016 . This second day of observations can be found under filename: TSUB0001.sb32720781.eb32773507.57646.24443702547

For the purpose of this tutorial, we only reduce and image the first data set. The second data set can be reduced in an identical way, pending the flagging, as the RFI conditions most likely changed. During the imaging stage you can then combine both reduced data sets.

To download the data, fill in your email, select either the "SDM-BDF (all files)" or “MS” option, and check the box next to the data sets that you want. The practical difference between selecting the MeasurementSet (MS) and Science Data Model (SDM) is that for MS the first step in the data reduction described below, namely reading in the SDM data to save it as a MeasurementSet, is already done by the archival engine. Note that you can also opt to download the data as a tar-file by clicking the appropriate box.

Optionally, you can request to discard data marked as ‘bad’ by clicking the box “Apply Telescope Flags”, which gets rid of data taken during times of instrument calibration, shadowing, slewing, etc. However, it also applies all flags on the science data that were automatically created during the observations. Therefore, a safer option is to not apply any flags before downloading the data, but first inspect and subsequently apply the flags generated during the observations using “flagcmd”, as part of the data reduction plan.

Click "Get My Data" will forward you to the next page, where you should choose the delivery method (either downloading over the internet, or sending home a hard-drive with the data). If you opt to retrieve the data over the internet, wait until you get an email confirming that the data is ready for download.

Loading data into CASA

Start CASA by typing

casa

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 5.0.0. While older version may work as well for the purpose of this tutorial, it is good to visit this page for instruction on download and installation of the latest version of CASA. We will begin by importing our data from the binary format (SDM-BDF) into the MeasurementSet format, which is the standard for CASA data. For this, we use importevla:

# In CASA
importevla(asdm='TSUB0001.sb32720781.eb32763188.57645.263958564814',vis='MG0414_d1.ms',flagpol=False,applyflags=False,savecmds=True,outfile='flagfile.txt')

In this case, we do not apply the flags that were created as part of the observations, but we will write them out to a flagfile.txt file. We will inspect and apply the flags as follows:

# In CASA
flagcmd(vis='MG0414_d1.ms',action='plot')
flagcmd(vis='MG0414_d1.ms',action='apply')

As an alternative to flagcmd, because we saved the flags to flagfile.txt, we can also use

# In CASA
flagdata(vis='MG0414_d1.ms',mode='list',inpfile='flagfile.txt',action='apply')

to apply the flags.

One of the flag commands is to clip all data points that have a 0-value, as these most likely have not recorded any science data. This is normally done with the settings correlation=`ABS_RR’ and correlation=`ABS_LL’. Because the VLA P-band and 4-band systems use linear XX and YY polarisation, rather than the circular RR and LL polarisation of the other VLA bands, you will encounter an error message when running flagcmd. We can get around this issue by manually clipping the 0-data for the XX and LL polarization with flagdata:

# In CASA
flagdata(vis='MG0414_d1.ms',mode='clip',correlation='ABS_XX,ABS_YY',clipzeros=True,action='apply')

Note: It is good practice to also carefully read the Operator Log that was created by the operator on duty during the observations. This can provide additional information on data that should be flagged manually during the data reduction stage. These Operator logs can be found here.

Now that the data is read in and the first flagging is performed, we will inspect the content of our data set using listobs

# In CASA
listobs(vis='MG0414_d1.ms',listfile='listobs.txt')

This normally plots an overview of the data in the CASA logger, but with the commmand listfile=`listobs.txt’ this information is written out to the file listobs.txt.