M100 Band3 SingleDish 4.5: Difference between revisions
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We do not concatenate separate Execution Blocks, because we need to determine and apply day-by-day Jy/K conversion factor. | We do not concatenate separate Execution Blocks, because we need to determine and apply day-by-day Jy/K conversion factor. | ||
Revision as of 03:01, 26 October 2015
This page is currently under construction.
DO NOT USE IT. For the release CASA version (4.3), please go go M100_Band3_SingleDish_4.3
To navigate the CASAguides pages, visit [http://casaguides.nrao.edu/ casaguides.nrao.edu ]
- This guide requires CASA 4.5 and assumes that you have downloaded M100_Band3_TP_UncalibratedData.tgz as described in M100_Band3#Obtaining_the_Data
- Details of the ALMA observations are described at M100_Band3
- This portion of the guide covers calibration and imaging starting from the raw visibility data.
Overview
This portion of CASA Guide will cover the data reduction of the Total Power (TP) array observations of M100. The data consist of "amplitude calibrator" datasets (containing observational data of the quasar 3C279) and "science" datasets containing data for the science target M100. The data are reduced in the following steps:
- Both the amplitude calibrator and science datasets are calibrated into units of Kelvins.
- The radio continuum emission from 3C279 in the amplitude calibrator data are used to derive the Jansky/Kelvin (Jy/K) factors for individual days and frequencies (spectral windows).
- Using the derived Jy/K values, the science data are calibrated into Jy/beam units.
- The calibrated science data are imaged into a data cube.
The combination of the resultant image with the interferometric (12-m array and 7-m array) data is explained in a separate page, valid for both CASA versions 4.3 and 4.5 M100_Band3_Combine_4.3.
This guide is designed for CASA 4.5.0.
Confirm Your Version of CASA
This guide has been written for CASA release 4.5.0. Please confirm your version before proceeding.
# In CASA
version = casadef.casa_version
print "You are using " + version
if (version < '4.5.0'):
print "YOUR VERSION OF CASA IS TOO OLD FOR THIS GUIDE."
print "PLEASE UPDATE IT BEFORE PROCEEDING."
else:
print "Your version of CASA is appropriate for this guide."
Summary of Datasets
The observations were made on the 1st, 5th, 7th, and 17th July 2014, using two or three 12-m antennas and the ACA correlator. The table below indicates the ID's of the Execution Blocks, their start and end times, and the antennas in the array. There are nine science datasets (i.e., two or three per day).
#Science uid___A002_X85c183_X36f Observed from 2014-07-01T21:51:26.2 to 2014-07-01T22:40:28.4 (UTC) DA61, PM03, PM04 uid___A002_X85c183_X60b Observed from 2014-07-01T22:43:50.0 to 2014-07-01T23:32:39.6 (UTC) DA61, PM03, PM04 uid___A002_X8602fa_X2ab Observed from 2014-07-05T23:58:03.6 to 2014-07-06T00:46:52.0 (UTC) PM02, PM03, PM04 uid___A002_X8602fa_X577 Observed from 2014-07-06T00:55:17.8 to 2014-07-06T01:44:07.3 (UTC) PM02, PM03, PM04 uid___A002_X864236_X2d4 Observed from 2014-07-07T23:03:48.1 to 2014-07-07T23:53:47.9 (UTC) PM03, PM04 uid___A002_X864236_X693 Observed from 2014-07-07T23:56:09.6 to 2014-07-08T00:46:07.1 (UTC) PM03, PM04 uid___A002_X86fcfa_Xd9 Observed from 2014-07-17T20:55:15.5 to 2014-07-17T21:44:06.1 (UTC) DV10, PM03, PM04 uid___A002_X86fcfa_X664 Observed from 2014-07-17T22:24:17.3 to 2014-07-17T23:13:08.0 (UTC) DV10, PM03, PM04 uid___A002_X86fcfa_X96c Observed from 2014-07-17T23:23:37.0 to 2014-07-18T00:12:25.3 (UTC) DV10, PM03, PM04
Here we define the list of the Execution Block ID's of the science datasets, to facilitate data reduction using for-loops.
# In CASA
basename_science = ['uid___A002_X85c183_X36f', 'uid___A002_X85c183_X60b',
'uid___A002_X8602fa_X2ab', 'uid___A002_X8602fa_X577',
'uid___A002_X864236_X2d4', 'uid___A002_X864236_X693',
'uid___A002_X86fcfa_Xd9', 'uid___A002_X86fcfa_X664',
'uid___A002_X86fcfa_X96c']
Calibration into Brightness Temperature in Kelvins
In this section, the data are calibrated into brightness temperature in units of K.
This is done in the steps that are described below, using uid___A002_X85c183_X36f as an example. If you wish to simply calibrate the data without working through the steps, you can instead execute the script A002_X85c183_X36f.ms.scriptForSDCalibration.py using the execfile command.
# In CASA
execfile('A002_X85c183_X36f.ms.scriptForSDCalibration.py')
The other datasets also need to be calibrated using the following scripts; A002_X85c183_X60b.ms.scriptForSDCalibration.py, A002_X8602fa_X2ab.ms.scriptForSDCalibration.py, A002_X8602fa_X577.ms.scriptForSDCalibration.py, A002_X8602fa_X2d4.ms.scriptForSDCalibration.py, A002_X864236_X693.ms.scriptForSDCalibration.py, A002_X86fcfa_Xd9.ms.scriptForSDCalibration.py, A002_X86fcfa_X664.ms.scriptForSDCalibration.py, and A002_X86fcfa_X96c.ms.scriptForSDCalibration.py. The following piece of code executes these scripts.
# In CASA
for basename in basename_science:
scriptname = basename[6:]+'.ms.scriptForSDCalibration.py'
if basename == 'uid___A002_X85c183_X36f':
print 'Calibration procedure for %s is described in the Guide.' % basename
print 'Please go through the Guide, '
print 'or run execfile("%s") instead.' % scriptname
else:
print 'Calibrating %s ...' % basename
execfile(scriptname)
Import the Data
The first thing to do is to convert the dataset into the CASA Measurement Set (MS) format. The raw data have been provided to you in the ASDM (ALMA Science Data Model). It is the native format of the data produced by the observatory but cannot be processed by CASA. The conversion from ASDM to MS is done with the task importasdm.
# In CASA
importasdm('uid___A002_X85c183_X36f',
asis='Antenna Station Receiver Source CalAtmosphere CalWVR',
bdfflags=False)
The converted dataset (with a suffix ".ms") is created. Then some sort of flags embedded in the binary files in the ASDM dataset (so-called "BDF flags") are transferred to MS using the command bdflags2MS.
# In CASA
os.system(os.environ['CASAPATH'].split()[0] + \
'/bin/bdflags2MS -f "COR DELA INT MIS SIG SYN TFB WVR ZER" ' + \
'uid___A002_X85c183_X36f uid___A002_X85c183_X36f.ms')
Initial Inspection
The usual first step is then to get some basic information about the data. We do this using the task listobs, which will output a detailed summary of each dataset supplied.
# In CASA
listobs(vis='uid___A002_X85c183_X36f.ms',
listfile='uid___A002_X85c183_X36f.ms.listobs')
The output will be sent to the CASA logger, and also written in a file named uid___A002_X85c183_X36f.ms.listobs. You can print the contents of the file to the terminal by typing:
# In CASA
os.system('cat uid___A002_X85c183_X36f.ms.listobs')
Alternatively you can use your favorite pager or editor, instead of "cat" command.
Here is an abridged example extracted from the output from listobs for uid___A002_X85c183_X36f:
Fields: 2
ID Code Name RA Decl Epoch SrcId nRows
0 none J1215+1654 12:15:03.979140 +16.54.37.95680 J2000 0 14661
1 none M100 12:22:54.360000 +15.48.50.60000 J2000 1 61653
Spectral Windows: (25 unique spectral windows and 2 unique polarization setups)
SpwID Name #Chans Frame Ch0(MHz) ChanWid(kHz) TotBW(kHz) CtrFreq(MHz) BBC Num Corrs
0 WVR#NOMINAL 4 TOPO 184550.000 1500000.000 7500000.0 187550.0000 0 XX
1 ALMA_RB_03#BB_1#SW-01#FULL_RES 124 TOPO 91955.512 -15625.000 1937500.0 90994.5750 1 XX YY
2 ALMA_RB_03#BB_1#SW-01#CH_AVG 1 TOPO 90978.950 1734375.000 1734375.0 90978.9500 1 XX YY
3 ALMA_RB_03#BB_2#SW-01#FULL_RES 124 TOPO 93893.012 -15625.000 1937500.0 92932.0750 2 XX YY
4 ALMA_RB_03#BB_2#SW-01#CH_AVG 1 TOPO 92924.262 1937500.000 1937500.0 92924.2625 2 XX YY
5 ALMA_RB_03#BB_3#SW-01#FULL_RES 124 TOPO 102033.637 15625.000 1937500.0 102994.5750 3 XX YY
6 ALMA_RB_03#BB_3#SW-01#CH_AVG 1 TOPO 102986.762 1937500.000 1937500.0 102986.7625 3 XX YY
7 ALMA_RB_03#BB_4#SW-01#FULL_RES 124 TOPO 104033.637 15625.000 1937500.0 104994.5750 4 XX YY
8 ALMA_RB_03#BB_4#SW-01#CH_AVG 1 TOPO 104986.762 1937500.000 1937500.0 104986.7625 4 XX YY
9 ALMA_RB_03#BB_1#SW-01#FULL_RES 128 TOPO 101942.187 -15625.000 2000000.0 100950.0000 1 XX YY
10 ALMA_RB_03#BB_1#SW-01#CH_AVG 1 TOPO 100926.562 1781250.000 1781250.0 100926.5625 1 XX YY
11 ALMA_RB_03#BB_2#SW-01#FULL_RES 128 TOPO 103757.337 -15625.000 2000000.0 102765.1500 2 XX YY
12 ALMA_RB_03#BB_2#SW-01#CH_AVG 1 TOPO 102741.712 1781250.000 1781250.0 102741.7125 2 XX YY
13 ALMA_RB_03#BB_3#SW-01#FULL_RES 128 TOPO 111814.962 15625.000 2000000.0 112807.1500 3 XX YY
14 ALMA_RB_03#BB_3#SW-01#CH_AVG 1 TOPO 112783.712 1781250.000 1781250.0 112783.7125 3 XX YY
15 ALMA_RB_03#BB_4#SW-01#FULL_RES 128 TOPO 113689.962 15625.000 2000000.0 114682.1500 4 XX YY
16 ALMA_RB_03#BB_4#SW-01#CH_AVG 1 TOPO 114658.712 1781250.000 1781250.0 114658.7125 4 XX YY
17 ALMA_RB_03#BB_1#SW-01#FULL_RES 4080 TOPO 101945.850 -488.281 1992187.5 100950.0000 1 XX YY
18 ALMA_RB_03#BB_1#SW-01#CH_AVG 1 TOPO 100949.756 1992187.500 1992187.5 100949.7559 1 XX YY
19 ALMA_RB_03#BB_2#SW-01#FULL_RES 4080 TOPO 103761.000 -488.281 1992187.5 102765.1500 2 XX YY
20 ALMA_RB_03#BB_2#SW-01#CH_AVG 1 TOPO 102764.906 1992187.500 1992187.5 102764.9059 2 XX YY
21 ALMA_RB_03#BB_3#SW-01#FULL_RES 4080 TOPO 111811.300 488.281 1992187.5 112807.1500 3 XX YY
22 ALMA_RB_03#BB_3#SW-01#CH_AVG 1 TOPO 112806.906 1992187.500 1992187.5 112806.9059 3 XX YY
23 ALMA_RB_03#BB_4#SW-01#FULL_RES 4080 TOPO 113686.300 488.281 1992187.5 114682.1500 4 XX YY
24 ALMA_RB_03#BB_4#SW-01#CH_AVG 1 TOPO 114681.906 1992187.500 1992187.5 114681.9059 4 XX YY
Sources: 48
ID Name SpwId RestFreq(MHz) SysVel(km/s)
0 J1215+1654 0 - -
0 J1215+1654 25 - -
0 J1215+1654 26 - -
0 J1215+1654 27 - -
0 J1215+1654 1 - -
0 J1215+1654 2 - -
0 J1215+1654 3 - -
0 J1215+1654 4 - -
0 J1215+1654 5 - -
0 J1215+1654 6 - -
0 J1215+1654 7 - -
0 J1215+1654 8 - -
0 J1215+1654 9 - -
0 J1215+1654 10 - -
0 J1215+1654 11 - -
0 J1215+1654 12 - -
0 J1215+1654 13 - -
0 J1215+1654 14 - -
0 J1215+1654 15 - -
0 J1215+1654 16 - -
0 J1215+1654 17 100950 0
0 J1215+1654 18 100950 0
0 J1215+1654 19 102794.1 0
0 J1215+1654 20 102794.1 0
0 J1215+1654 21 112794.1 0
0 J1215+1654 22 112794.1 0
0 J1215+1654 23 114669.1 0
0 J1215+1654 24 114669.1 0
1 M100 0 - -
1 M100 25 - -
1 M100 26 - -
1 M100 27 - -
1 M100 9 - -
1 M100 10 - -
1 M100 11 - -
1 M100 12 - -
1 M100 13 - -
1 M100 14 - -
1 M100 15 - -
1 M100 16 - -
1 M100 17 100950 0
1 M100 18 100950 0
1 M100 19 102794.1 0
1 M100 20 102794.1 0
1 M100 21 112794.1 0
1 M100 22 112794.1 0
1 M100 23 114669.1 0
1 M100 24 114669.1 0
Antennas: 3:
ID Name Station Diam. Long. Lat. Offset from array center (m) ITRF Geocentric coordinates (m)
East North Elevation x y z
0 DA61 A075 12.0 m -067.45.17.9 -22.53.21.4 -4.5609 -499.7012 23.0322 2225072.419944 -5440148.858968 -2481499.171703
1 PM03 T701 12.0 m -067.45.18.8 -22.53.22.2 -29.1265 -522.7875 22.2052 2225045.995589 -5440149.141967 -2481520.118569
2 PM04 T703 12.0 m -067.45.16.2 -22.53.23.9 42.8797 -575.6910 21.7763 2225104.700870 -5440102.471978 -2481568.689518
From this output you can for example see the followings.
- From "Data records" section (not shown here): The execution consists of 15 scans with various scan intents.
- Scans 1 and 2 are pointing and sideband gain ratio calibrations (done interferometrically), which need to be done prior to observing the target, on the quasar J1215+1654.
- Scans 3 and 4 are interferometric delay and system noise temperature (Tsys) measurements also on J1215+1654 (they are in principle unnecessary; just a hack to make things happen on the telescope control software).
- Scans 6, 7, 9, 10, 12, 13, and 15, whose scan intents contain "OBSERVE_TARGET", are for raster mapping of the target M100. The associated spectral window (SPW) ID's are 0 and 17-24.
- Scans 5, 8, 11, and 14 with scan intents "CALIBRATE_ATMOSPHERE" are Tsys measurements for M100. The SPW ID's for Tsys scans are 0 and 9-16.
- From "Spectral Windows" section:
- SPW 0 contains the Water Vapor Radiometer (WVR) data, which are taken for all scans, but which are not used here.
- SPW's 1, 3, 5, and 7 are placed on standard continuum frequencies and are used for the pointing scan only.
- SPW's 9, 11, 13, and 15 cover the same frequency range as the science SPW's (see the next bullet) and are used to measure the Tsys used for calibration. They have 128 spectral channels (15.6 MHz spacing) in 2000 MHz bandwidth.
- SPW's 17, 19, 21, and 23 cover the frequency ranges used for the actual science observations. They have 4080 spectral channels (488 kHz spacing) in 1992 MHz bandwidth. Hereafter these SPW's are referred to as "science" SPW's. The target line, CO J=1-0, is placed in SPW 23, the corresponding Tsys is taken from SPW 15.
- The remaining, even-number SPW's contain channel-averages of the actually used SPW's (e.g., SPW 24 corresponds to SPW 23 averaged into one channel).
- From "Antennas" section: Three 12-m antennas (DA61, PM03, and PM04) were used for the observations.
The scan pattern of the raster mapping can be visualized by issueing the following command. A plot will be shown in a window and also saved as a PNG file uid___A002_X85c183_X36f.ms.sampling.png.
<figure id="X36f.TPSampling.png">
</figure>
# In CASA
aU.getTPSampling(vis='uid___A002_X85c183_X36f.ms',
showplot=True,
plotfile='uid___A002_X85c183_X36f.ms.sampling.png')
Inspect the System Noise Temperature
Let's start by checking the Tsys. We use the task gencal to extract the Tsys into a CASA calibration table. This table is only used for plotting and for letting CASA figure out the mapping between the science and Tsys SPW's (see "Tsys Calibration" section).
# In CASA
gencal(vis='uid___A002_X85c183_X36f.ms',
caltable='uid___A002_X85c183_X36f.ms.tsys',
caltype='tsys')
The generated Tsys calibration table can be plotted using the task plotbandpass and the checkCalTable function of the Analysis Utilities. The generated plots are saved in the directories uid___A002_X85c183_X36f.ms.tsys.plots.overlayTime and uid___A002_X85c183_X36f.ms.tsys.plots.
<figure id="X36f.Tsys.overlayTime.png">
</figure>
<figure id="X36f.Tsys.overlayAntenna.png.png">
</figure>
# In CASA
plotbandpass(caltable='uid___A002_X85c183_X36f.ms.tsys',
overlay='time',
xaxis='freq',
yaxis='amp',
subplot=22,
buildpdf=False,
interactive=False,
showatm=True,
pwv='auto',
chanrange='5~123',
showfdm=True,
field='',
figfile='uid___A002_X85c183_X36f.ms.tsys.plots.overlayTime/uid___A002_X85c183_X36f.ms.tsys')
es.checkCalTable('uid___A002_X85c183_X36f.ms.tsys',
msName='uid___A002_X85c183_X36f.ms',
interactive=False)
A Priori Flagging
Now we do some a-priori flagging of the edge channels. Although the "science" SPW's have 1992 MHz bandwidths with 4080 spectral channels, their edge channels are very noisy, because each intermediate frequency signal path (baseband) is equipped with a bandpass filter of ~1.8 GHz width. We flag 120 channels on each side of the "science" SPW's. As a result 3840 channels (1875 MHz bandwidth), i.e., the same number of channels and bandwidth as FDM SPW's in the 12-m array data, remain in each SPW.
# In CASA
for ant in ['DA61', 'PM03', 'PM04']:
sdflag(infile='uid___A002_X85c183_X36f.ms.%s.asap' % ant,
mode='manual',
spw='17:0~119;3960~4079,19:0~119;3960~4079,21:0~119;3960~4079,23:0~119;3960~4079',
overwrite = True)
Tsys Calibration
We calibrate the data into brightness temperature in units of K, using the equation Ta* = Tsys*(ON-OFF)/OFF, where ON and OFF are the data on-source (i.e., during the raster scanning) and off-source (on a reference position where only background emission exists), respectively. The calibration is done by the task Template:Sdcal2. It requires the list of the "science" SPW's and Tsys SPW's, and the correspondence between them. We can tell from the listobs (or sdlist) output that Tsys SPW's 9, 11, 13, and 15 correspond to "science" SPW's 17, 19, 21, and 23, respectively; but the function tsysspwmap helps to map Tsys SPW's to science SPW's in an automated way:
# In CASA
from recipes.almahelpers import tsysspwmap
tsysmap = tsysspwmap(vis='uid___A002_X85c183_X36f.ms',
tsystable='uid___A002_X85c183_X36f.ms.tsys')
The obtained variable tsysmap is a list which looks like as follows. The N-th (counted from 0) item indicates the Tsys SPW corresponding to the SPW ID N, e.g., the 23rd item is 15, thus the Tsys SPW 15 corresponds to the science SPW 23.
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 9, 11, 11, 13, 13, 15, 15, 9, 9, 11, 11, 13, 13, 15, 15]
Now this information needs to be translated into a dictionary variable which Template:Sdcal2 requires. The mapping between the science SPW's (17, 19, 21, and 23) and Tsys SPW's is given by the following piece of code:
# In CASA
spwmap = {}
for i in [17, 19, 21, 23]:
if not tsysmap[i] in spwmap.keys():
spwmap[tsysmap[i]] = []
spwmap[tsysmap[i]].append(i)
The obtained correspondence between Tsys and science SPW's is stored in the dictionary spwmap which looks like the following:
{9: [17], 11: [19], 13: [21], 15: [23]}
This is given to sdcal2, along with the comma-separated lists of SPW's. An important parameter of the task is calmode. In case of the science dataset uid___A002_X85c183_X36f, calmode should be set to 'ps,tsys,apply'. 'ps' means that the data at dedicated reference position are used as "OFF" for the OFF subtraction, (ON-OFF)/OFF. 'tsys' is to calibrate the data using Tsys. 'apply' is to apply both calibrations above (OFF-subtraction and Tsys).
# In CASA
for ant in ['DA61', 'PM03', 'PM04']:
sdcal2(infile='uid___A002_X85c183_X36f.ms.%s.asap' % ant,
calmode='ps,tsys,apply',
spw='9,11,13,15,17,19,21,23',
tsysspw='9,11,13,15',
spwmap=spwmap,
outfile='uid___A002_X85c183_X36f.ms.%s.asap.cal' % ant,
overwrite=True)
A new ASAP dataset with additional suffix '.cal' is generated for each antenna. Before proceeding to the next step, we can plot the calibrated spectra using the SDcheckSpectra function of the Analysis Utilities. PNG files will be created in, e.g., uid___A002_X85c183_X36f.ms.DA61.asap.cal.plots directory. You will find the CO line in SPW 23 of the science datasets.
<figure id="X36f.spw23.cal.png">
</figure>
# In CASA
for ant in ['DA61', 'PM03', 'PM04']:
es.SDcheckSpectra('uid___A002_X85c183_X36f.ms.%s.asap.cal' % ant,
spwIds='17,19,21,23',
interactive=False)
Note: For amplitude calibrator datasets, (as opposed to science target maps) the calmode option for sdcal2 should be 'otfraster,tsys,apply' ('otfraster' instead of 'ps'). 'otfraster' tells the task to use both ends of each raster row as "OFF" data (as opposed to a dedicated "OFF" sky measurement). This is because temporal fluctuation of atmospheric emission is the dominant source of noise in radio continuum observations, and hence the "OFF" needs to be as close as possible to the "ON" in time (and spatial location).
Application of Non-Linearity Correction Factor
In the period of Early Science Cycle 1/2, single-dish data taken with the ACA correlator suffer from non-linearity which originates in the digital signal processing. Its impact was thoroughly studied both experimentally and theoretically, and it was concluded that multiplication by a correction factor of 1.25 yields an amplitude accuracy of +/-5%. This correction is done by the task sdscale. A new ASAP dataset with additional suffix '.nlc' (for Non-Linearity Correction) will be created for each antenna.
# In CASA
for ant in ['DA61', 'PM03', 'PM04']:
sdscale(infile='uid___A002_X85c183_X36f.ms.%s.asap.cal' % ant,
outfile='uid___A002_X85c183_X36f.ms.%s.asap.cal.nlc' % ant,
factor=1.25)
An upgrade of the ACA correlator hardware and software is expected to solve the non-linearity issue. Thus this step will become unnecessary for data taken in the near future.
Baseline Subtraction (only for Science Datasets)
Note: this step is appropriate for spectral line observations (science datasets) but should not be done for the continuum observations (amplitude calibrator datasets) -- it would eliminate the continuum emission!
We will subtract spectral baselines using the task sdbaseline. With the option "maskmode='auto'", the task automatically finds line features from individual spectra and excludes them from baseline fitting. A caveat is that the line finding may not work very well in some cases, including this dataset (see "Subtract a Residual Background from the Image" section below).
# In CASA
for ant in ['DA61', 'PM03', 'PM04']:
sdbaseline(infile='uid___A002_X85c183_X36f.ms.%s.asap.cal.nlc' % ant,
spw='17,19,21,23',
maskmode='auto',
thresh=5.0,
avg_limit=4,
blfunc='poly',
order=1,
outfile='uid___A002_X85c183_X36f.ms.%s.asap.cal.nlc.bl' % ant,
overwrite=True)
Datasets with yet another suffix ".bl" are generated. The spectra can be checked using SDcheckSpectra which we have already used in a previous step.
<figure id="X36f.spw23.cal.bl.png">
</figure>
# In CASA
for ant in ['DA61', 'PM03', 'PM04']:
es.SDcheckSpectra('uid___A002_X85c183_X36f.ms.%s.asap.cal.nlc.bl' % ant,
spwIds='17,19,21,23',
interactive=False)
We do not concatenate separate Execution Blocks, because we need to determine and apply day-by-day Jy/K conversion factor.
Note that one of the antennas, PM02, had a problem in the first baseband (SPW 17) on 2014-07-05, and its impact may not be specific to this one SPW (e.g., it may have resulted in poor pointing calibration). Therefore the PM02 data should be excluded from the concatenation for the affected datasets: uid___A002_X8602fa_Xc3 (amplitude calibrator), uid___A002_X8602fa_X2ab (science), and uid___A002_X8602fa_X577 (science).
Optionally Image the Amplitude Calibrator and Measure the Value of Jy/K
At this stage you should have run all 9 science datasets through their respective scriptForSDCalibration.py, i.e., all 9 datasets have been calibrated into units of Kelvins. The next step is to apply Jy/K conversion factors that have been derived from observations of an amplitude calibrator (one Jy/K value determined per day and per SPW). The resulting Jy/K values are given at the end of this section.
If you want to know details of how the Jy/K values are determined, a summary is given below. Otherwise, you can skip ahead to the next section M100_Band3_SingleDish_4.5#Convert the Science Target Units from Kelvin to Jansky to continue the calibration.
Determination of the Jy/K conversion factor is achieved by imaging a source whose continuum flux is known and measuring the observed brightness temperature. One observation of this source - the amplitude calibrator - was made per day, giving a total of four datasets:
#Amplitude Calibrator uid___A002_X85c183_X895 Observed from 2014-07-01T23:35:23.1 to 2014-07-02T00:07:54.6 (UTC) DA61, PM03, PM04 uid___A002_X8602fa_Xc3 Observed from 2014-07-05T23:21:25.6 to 2014-07-05T23:53:41.0 (UTC) PM02, PM03, PM04 uid___A002_X864236_Xe1 Observed from 2014-07-07T22:27:35.4 to 2014-07-07T23:01:05.7 (UTC) PM03, PM04 uid___A002_X86fcfa_X3ae Observed from 2014-07-17T21:48:30.0 to 2014-07-17T22:20:52.2 (UTC) DV10, PM03, PM04
The true size of the beam (point spread function), on which the Jy/K value depends, in a map is determined be several ingredients.
- The intrinsic beam size is determined by the antenna diameter and optics (and of course wavelength). Here we assume the intrinsic beam size of 1.13*lambda/D, where D=12 [m] is the diameter of the antennas, and the factor 1.13 is derived from the optics design of the ALMA 12-m antennas.
- The effective beam size is broadened by the following causes:
- The scanning pattern used to observe the map (i.e., sample spacing in both directions).
- The method used to grid the individual spectra into a map/cube.
The Analysis Utilities provides several tools to get estimates of these.
If you wish to try this out for yourself, you will first need to download the raw amplitude calibrator data file M100_Band3_TP_ampcal_UncalibratedData.tgz. How to obtain the data is described at M100_Band3#Obtaining the Data. Once you have unpacked the data you will have a directory called M100_Band3_TP_ampcal_UncalibratedData. In this directory, execute the scripts for individual datasets (A002_X85c183_X895.ms.scriptForSDCalibration.py, A002_X8602fa_Xc3.ms.scriptForSDCalibration.py, A002_X864236_Xe1.ms.scriptForSDCalibration.py, and A002_X86fcfa_X3ae.ms.scriptForSDCalibration.py) to calibrate all four calibrator datasets (e.g., execfile('A002_X85c183_X895.ms.scriptForSDCalibration.py') in CASA). Then, run the script "ScriptForImagingAmpCalAndDerivingJyPerK.py", which uses for-loops to image the amplitude calibrator and calculate the Jy/K values (run execfile('ScriptForImagingAmpCalAndDerivingJyPerK.py') in CASA).
The resulting Jy/K values are as follows. Note that the values for individual antennas are averaged for each day, each SPW, because the antennas used for the Science observations were not necessarily available for the corresponding amplitude calibrator observations.
#Date Amp_Cal_Dataset SPW17 SPW19 SPW21 SPW23 2014-07-01 uid___A002_X85c183_X895 41.37 42.39 43.45 42.82 2014-07-05 uid___A002_X8602fa_Xc3 40.99 42.74 40.08 42.09 2014-07-07 uid___A002_X864236_Xe1 39.42 42.08 40.18 40.82 2014-07-17 uid___A002_X86fcfa_X3ae 43.49 43.70 42.01 43.04
Convert the Science Target Units from Kelvin to Jansky
The science data, which have been calibrated into brightness temperature in units of K, are now converted into Jy units by multiplying the Jy/K factors derived above. This step is done by the script "ScriptForJyPerKConversion.py" (run execfile('ScriptForJyPerKConversion.py') in CASA).
Define the lists of SPWs and corresponding Jy/K factors to process the data using for-loops.
# In CASA
# List the science spws
spwlist = [17, 19, 21, 23]
# List the Jy/K values (corresponding to the list of the science spws above)
# that were the output from ScriptForImagingAmpCalAndDerivingJyPerK.py
jyperklist0701 = [41.37, 42.39, 43.45, 42.82]
jyperklist0705 = [40.99, 42.74, 40.08, 42.09]
jyperklist0707 = [39.42, 42.08, 40.18, 40.82]
jyperklist0717 = [43.49, 43.70, 42.01, 43.04]
Make a copy of calibrated dataset, with an additional suffix ".jy", and multiply the Jy/K value for each SPW. This is done by the scaleAutocorr function of Analysis Utilities.
# In CASA
# Data taken on 2014-07-01
for name in ['uid___A002_X85c183_X36f', 'uid___A002_X85c183_X60b']:
os.system('rm -Rf %s.ms.cal.jy' % name)
os.system('cp -Rf %s.ms.cal %s.ms.cal.jy' % (name, name))
for (spw, jyperk) in zip(spwlist, jyperklist0701):
aU.scaleAutocorr(vis='%s.ms.cal.jy' % name, scale=jyperk, spw=spw)
# Data taken on 2014-07-05
for name in ['uid___A002_X8602fa_X2ab', 'uid___A002_X8602fa_X577']:
os.system('rm -Rf %s.ms.cal.jy' % name)
os.system('cp -Rf %s.ms.cal %s.ms.cal.jy' % (name, name))
for (spw, jyperk) in zip(spwlist, jyperklist0705):
aU.scaleAutocorr(vis='%s.ms.cal.jy' % name, scale=jyperk, spw=spw)
# Data taken on 2014-07-07
for name in ['uid___A002_X864236_X2d4', 'uid___A002_X864236_X693']:
os.system('rm -Rf %s.ms.cal.jy' % name)
os.system('cp -Rf %s.ms.cal %s.ms.cal.jy' % (name, name))
for (spw, jyperk) in zip(spwlist, jyperklist0707):
aU.scaleAutocorr(vis='%s.ms.cal.jy' % name, scale=jyperk, spw=spw)
# Data taken on 2014-07-17
for name in ['uid___A002_X86fcfa_Xd9', 'uid___A002_X86fcfa_X664',
'uid___A002_X86fcfa_X96c']:
os.system('rm -Rf %s.ms.cal.jy' % name)
os.system('cp -Rf %s.ms.cal %s.ms.cal.jy' % (name, name))
for (spw, jyperk) in zip(spwlist, jyperklist0717):
aU.scaleAutocorr(vis='%s.ms.cal.jy' % name, scale=jyperk, spw=spw)
Import "Analysis Utilities"
The Analysis_Utilities package will be used for the following processes. Import the package and instantiate the stuffForScienceDataReduction class therein.
# In CASA
import analysisUtils as aU
es = aU.stuffForScienceDataReduction()
Image the Science Target
Now all the science datasets have been calibrated into Jy units. The next (final) step is to image the science data. This step is done by the script "ScriptForImagingScienceTarget.py" (run execfile('ScriptForImagingScienceTarget.py') in CASA).
Define the list of the calibrated science datasets, which the CASA task sdimaging accepts.
# In CASA
sciencedata = [('%s.ms.cal.jy' % name) for name in basename_science]
Obtain the data sampling and determine the cell spacing and map size based on the mean frequency of the target SPW. This part is in principle the same as the corresponding procedure in the "Optionally Image the Amplitude Calibrator and Measure the Value of Jy/K" section. Please refer to the script "ScriptForImagingAmpCalAndDerivingJyPerK.py" used there.
# In CASA
fwhmfactor = 1.13
diameter = 12
xSampling, ySampling, maxsize = aU.getTPSampling(sciencedata[0], showplot=False)
spw = 23
msmd.open(sciencedata[0])
freq = msmd.meanfreq(spw)
msmd.close()
print "SPW %d: %.3f GHz" % (spw, freq*1e-9)
theorybeam = aU.primaryBeamArcsec(frequency=freq*1e-9,
fwhmfactor=fwhmfactor,
diameter=diameter)
cell = theorybeam/9.0
imsize = int(round(maxsize/cell)*2)
Now the data are imaged. The velocity channel maps of the CO J=1-0 line (restfreq='115.271201800GHz') are created as a data cube which covers the velocity range of 1400-1700 km/s at a spacing of 5 km/s (start='1400km/s', width='5km/s', nchan=70).
Note: The Jy/K value depends on the parameters of gridding convolution (i.e., "gridfunction", "cell", and function-specific parameters ["convsupport" for gridfunction='SF']). That is, the same gridding parameters should be used for both amplitude calibrator and science images -- otherwise the calibration into Jy unit becomes invalid.
# In CASA
sdimaging(infiles=sciencedata,
field='M100',
spw='%d' % spw, #sciencespw
nchan=70,
mode='velocity',
start='1400km/s',
width='5km/s',
veltype='radio',
outframe='lsrk',
restfreq='115.271201800GHz',
gridfunction='SF',
convsupport=6,
stokes='',
phasecenter='J2000 12h22m54.9 +15d49m15',
ephemsrcname='',
imsize=imsize,
cell=str(cell)+'arcsec',
overwrite=True,
outfile='M100_TP_CO_cube.image')
The produced image has the brightness unit of K in the image header, which is not correct. Modify the header using the task imhead.
# In CASA
imhead(imagename='M100_TP_CO_cube.image',
mode='put',
hdkey='bunit',
hdvalue='Jy/beam')
(Optionally) Subtract a Residual Background from the Image
Now you can browse the data cube using the viewer.
# In CASA
viewer('M100_TP_CO_cube.image')
If you plot the line profile using the viewer, you may notice that the background (i.e., line-free channels) level is slightly negative. To correct this, spectral baselines are subtracted from the image using the task imcontsub.
# In CASA
imcontsub(imagename='M100_TP_CO_cube.image',
linefile='M100_TP_CO_cube.bl.image',
contfile='M100.ignorethis.image',
fitorder=1,
chans='0~7;62~69')
os.system('rm -rf M100.ignorethis.image')
The main cause of the negative baseline is the following. The task sdbaseline with maskmode='auto' is supposed to find lines from the data and exclude the detected lines from baseline fitting. However, the CO line from M100 is not bright enough to be detected from individual spectra (1-s integration), and hence sdbaseline included the velocity ranges of the line into baseline fitting. The impact of this effect would be smaller if the line is brighter, or narrower w.r.t. the correlator bandwidth.
Add Restoring Beam Header Information to the Science Image
The image does not have the beam size, which is necessary for combining the image with interferometric data, in the header. The beam size (including the broadening due to gridding convolution and data sampling) is calculated using the function sfBeam of the Analysis Utilities and then written into the header using the functions of the ia tool.
# In CASA
minor, major, fwhmsfBeam, sfBeam = aU.sfBeam(frequency=freq*1e-9,
pixelsize=cell,
convsupport=6,
img=None, #to use Gaussian theorybeam
stokes='both',
xSamplingArcsec=xSampling,
ySamplingArcsec=ySampling,
fwhmfactor=fwhmfactor,
diameter=diameter)
ia.open('M100_TP_CO_cube.bl.image')
ia.setrestoringbeam(major=str(sfBeam)+'arcsec', minor=str(sfBeam)+'arcsec', pa='0deg')
ia.done()
Quick Look at the Results
Make a 0th moment (integrated intensity) map using the task immoments and browse it using the viewer.
<figure id="M100.CO.cube.bl.image.mom0.png">
</figure>
# In CASA
os.system('rm -rf *M100_TP_CO_cube.bl.image.mom0')
immoments(imagename='M100_TP_CO_cube.bl.image',
moments=[0],
axis='spectral',
chans='8~61',
outfile='M100_TP_CO_cube.bl.image.mom0')
viewer('M100_TP_CO_cube.bl.image.mom0')
Combination with 12m and 7m Array Data
If you wish to learn how to combine the Total Power data with interferometric 12m and 7m data, see the guide for Data Combination M100_Band3_Combine_4.3
Last checked on CASA Version 4.5.0.
