User talk:Preshanthj
CASA Guide G55.7+3.4
- This CASA Guide is designed for CASA v4.3.1 the current casa-release.
EVLA Wide-Band Wide-Field Imaging: G55.7 - CASA4.2
Overview
This CASA Guide describes the imaging of the supernova remnant G55.7+3.4.. The data were taken on August 23, 2010, in the first D-configuration for which the new wide-band capabilities of the WIDAR correlator were available. The 8-hour-long observation includes all available 1 GHz of bandwidth in L-band, from 1-2 GHz in frequency.
Obtaining the data
A copy of the data can be downloaded here: http://casa.nrao.edu/Data/EVLA/G55/G55.7+3.4_10s.ms.tar.gz
Note that this dataset is rather large: ~15GB
As a start, unzip and untar the data:
tar -xzvf G55.7+3.4_10s.ms.tar.gz
This will take a minute, but once it's complete, you will have a directory called G55.7+3.4_10s.ms which is the data. Online flags have been applied (which delete known bad data), some uninteresting scans removed, and the data time-averaged to 10 seconds. (The data were taken in D-configuration, where maximum baselines are 1 km, so one can safely average to 3s or even 10s to reduce data set size.) This is equivalent to what you would download from the archive if you requested time-averaging, scans 16~313, and application of the online flags.
You can also find the dataset in the NRAO archive. Note that it is 170 GB in raw form.
Averaging to 10 seconds and the removal of some scans which are not used in this tutorial reduces the size of the data set to around 15 GB; the addition of columns for model and corrected data (known as "scratch columns") during calibration will ultimately inflate the MS by a factor of a few in size (to around 45 GB).
Start and confirm your version of CASA
Start CASA by typing casapy on the command line. If you have not used CASA before, some helpful tips are available on the Getting Started in CASA page.
This guide has been written for CASA release 4.2.0. Please confirm your version before proceeding.
# In CASA
version = casalog.version()
print "You are using " + version
if (int(version.split()[3][1:-1]) < 32490):
print "\033[91m YOUR VERSION OF CASA IS TOO OLD FOR THIS GUIDE."
print "\033[91m PLEASE UPDATE IT BEFORE PROCEEDING."
else:
print "Your version of CASA is appropriate for this guide."
Preliminary data evaluation
As a first step, use listobs to have a look at the MS:
# In CASA
listobs('G55.7+3.4_10s.ms')
Note that throughout this tutorial, we will run tasks using the task(parameter=value) syntax. When called in this manner, all parameters not explicitly set will use their default values.
The logger output will look like this:
================================================================================
MeasurementSet Name: /lustre/pjaganna/evla/casa_guide_g55/G55.7+3.4_10s.ms MS Version 2
================================================================================
Observer: Dr. Sanjay Sanjay Bhatnagar Project: T.B.D.
Observation: EVLA
Data records: 7343848 Total elapsed time = 26697 seconds
Observed from 23-Aug-2010/01:00:24.0 to 23-Aug-2010/08:25:21.0 (UTC)
ObservationID = 0 ArrayID = 0
Date Timerange (UTC) Scan FldId FieldName nRows SpwIds Average Interval(s) ScanIntent
23-Aug-2010/01:00:24.0 - 01:01:05.0 16 1 J1925+2106 8008 [0,1,2,3,4,5,6,7] [9.64, 9.64, 9.64, 9.64, 9.64, 9.64, 9.64, 9.64] [CALIBRATE_PHASE.UNSPECIFIED]
01:01:05.0 - 01:02:35.0 17 1 J1925+2106 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_PHASE.UNSPECIFIED]
01:02:35.0 - 01:04:05.0 18 1 J1925+2106 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_PHASE.UNSPECIFIED]
01:04:05.0 - 01:05:34.0 19 1 J1925+2106 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [CALIBRATE_PHASE.UNSPECIFIED]
01:05:34.0 - 01:07:04.0 20 1 J1925+2106 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_PHASE.UNSPECIFIED]
01:07:10.0 - 01:08:34.0 21 2 G55.7+3.4 25064 [0,1,2,3,4,5,6,7] [9.38, 9.38, 9.38, 9.38, 9.38, 9.38, 9.38, 9.38] [OBSERVE_TARGET.UNSPECIFIED]
01:08:34.0 - 01:10:04.0 22 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
01:10:04.0 - 01:11:34.0 23 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
01:11:34.0 - 01:13:03.0 24 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
01:13:03.0 - 01:14:33.0 25 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
01:14:33.0 - 01:16:03.0 26 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
01:16:03.0 - 01:17:33.0 27 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
01:17:33.0 - 01:19:02.0 28 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
01:19:02.0 - 01:20:32.0 29 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
01:20:32.0 - 01:22:02.0 30 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
01:22:02.0 - 01:23:32.0 31 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
01:23:32.0 - 01:25:01.0 32 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
01:25:01.0 - 01:26:31.0 33 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
01:26:31.0 - 01:28:01.0 34 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
01:28:01.0 - 01:29:31.0 35 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
01:29:31.0 - 01:31:00.0 36 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
01:31:00.0 - 01:32:30.0 37 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
01:32:30.0 - 01:34:00.0 38 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
01:34:00.0 - 01:35:30.0 39 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
01:35:30.0 - 01:36:59.0 40 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
01:37:05.0 - 01:38:29.0 41 1 J1925+2106 25064 [0,1,2,3,4,5,6,7] [9.38, 9.38, 9.38, 9.38, 9.38, 9.38, 9.38, 9.38] [CALIBRATE_PHASE.UNSPECIFIED]
01:38:29.0 - 01:39:59.0 42 1 J1925+2106 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_PHASE.UNSPECIFIED]
01:39:59.0 - 01:41:28.0 43 1 J1925+2106 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [CALIBRATE_PHASE.UNSPECIFIED]
01:41:34.0 - 01:42:58.0 44 2 G55.7+3.4 25064 [0,1,2,3,4,5,6,7] [9.37, 9.37, 9.37, 9.37, 9.37, 9.37, 9.37, 9.37] [OBSERVE_TARGET.UNSPECIFIED]
01:42:58.0 - 01:44:28.0 45 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
01:44:28.0 - 01:45:58.0 46 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
01:45:58.0 - 01:47:28.0 47 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
01:47:28.0 - 01:48:57.0 48 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
01:48:57.0 - 01:50:27.0 49 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
01:50:27.0 - 01:51:57.0 50 2 G55.7+3.4 23400 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
01:51:57.0 - 01:53:27.0 51 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
01:53:27.0 - 01:54:56.0 52 2 G55.7+3.4 23400 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
01:54:56.0 - 01:56:26.0 53 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
01:56:26.0 - 01:57:56.0 54 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
01:57:56.0 - 01:59:26.0 55 2 G55.7+3.4 23400 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
01:59:26.0 - 02:00:55.0 56 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
02:00:55.0 - 02:02:25.0 57 2 G55.7+3.4 23608 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
02:02:25.0 - 02:03:55.0 58 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
02:03:55.0 - 02:05:25.0 59 2 G55.7+3.4 24648 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
02:05:25.0 - 02:06:54.0 60 2 G55.7+3.4 24440 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
02:06:54.0 - 02:08:24.0 61 2 G55.7+3.4 23400 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
02:08:24.0 - 02:09:54.0 62 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
02:09:54.0 - 02:11:23.0 63 2 G55.7+3.4 23400 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
02:11:30.0 - 02:12:53.0 64 1 J1925+2106 25064 [0,1,2,3,4,5,6,7] [9.27, 9.27, 9.27, 9.27, 9.27, 9.27, 9.27, 9.27] [CALIBRATE_PHASE.UNSPECIFIED]
02:12:53.0 - 02:14:23.0 65 1 J1925+2106 24024 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_PHASE.UNSPECIFIED]
02:14:23.0 - 02:15:52.0 66 1 J1925+2106 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [CALIBRATE_PHASE.UNSPECIFIED]
02:15:59.0 - 02:17:23.0 67 2 G55.7+3.4 23200 [0,1,2,3,4,5,6,7] [9.38, 9.38, 9.38, 9.38, 9.38, 9.38, 9.38, 9.38] [OBSERVE_TARGET.UNSPECIFIED]
02:17:23.0 - 02:18:52.0 68 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
02:18:52.0 - 02:20:22.0 69 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
02:20:22.0 - 02:21:52.0 70 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
02:21:52.0 - 02:23:22.0 71 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
02:23:22.0 - 02:24:52.0 72 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
02:24:52.0 - 02:26:21.0 73 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
02:26:21.0 - 02:27:51.0 74 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
02:27:51.0 - 02:29:21.0 75 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
02:29:21.0 - 02:30:51.0 76 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
02:30:51.0 - 02:32:20.0 77 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
02:32:20.0 - 02:33:50.0 78 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
02:33:50.0 - 02:35:20.0 79 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
02:35:20.0 - 02:36:50.0 80 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
02:36:50.0 - 02:38:19.0 81 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
02:38:19.0 - 02:39:49.0 82 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
02:39:49.0 - 02:41:19.0 83 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
02:41:19.0 - 02:42:49.0 84 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
02:42:49.0 - 02:44:18.0 85 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
02:44:18.0 - 02:45:47.0 86 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
02:45:54.0 - 02:47:18.0 87 1 J1925+2106 25064 [0,1,2,3,4,5,6,7] [9.38, 9.38, 9.38, 9.38, 9.38, 9.38, 9.38, 9.38] [CALIBRATE_PHASE.UNSPECIFIED]
02:47:18.0 - 02:48:48.0 88 1 J1925+2106 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_PHASE.UNSPECIFIED]
02:48:48.0 - 02:50:17.0 89 1 J1925+2106 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [CALIBRATE_PHASE.UNSPECIFIED]
02:50:23.0 - 02:51:47.0 90 2 G55.7+3.4 25064 [0,1,2,3,4,5,6,7] [9.38, 9.38, 9.38, 9.38, 9.38, 9.38, 9.38, 9.38] [OBSERVE_TARGET.UNSPECIFIED]
02:51:47.0 - 02:53:17.0 91 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
02:53:17.0 - 02:54:47.0 92 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
02:54:47.0 - 02:56:16.0 93 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
02:56:16.0 - 02:57:46.0 94 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
02:57:46.0 - 02:59:16.0 95 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
02:59:16.0 - 03:00:46.0 96 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
03:00:46.0 - 03:02:15.0 97 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
03:02:15.0 - 03:03:45.0 98 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
03:03:45.0 - 03:05:15.0 99 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
03:05:15.0 - 03:06:45.0 100 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
03:06:45.0 - 03:08:14.0 101 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
03:08:14.0 - 03:09:44.0 102 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
03:09:44.0 - 03:11:14.0 103 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
03:11:14.0 - 03:12:44.0 104 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
03:12:44.0 - 03:14:13.0 105 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
03:14:13.0 - 03:15:43.0 106 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
03:15:43.0 - 03:17:13.0 107 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
03:17:13.0 - 03:18:43.0 108 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
03:18:43.0 - 03:20:12.0 109 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
03:20:18.0 - 03:21:42.0 110 1 J1925+2106 25064 [0,1,2,3,4,5,6,7] [9.38, 9.38, 9.38, 9.38, 9.38, 9.38, 9.38, 9.38] [CALIBRATE_PHASE.UNSPECIFIED]
03:21:42.0 - 03:23:12.0 111 1 J1925+2106 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_PHASE.UNSPECIFIED]
03:23:12.0 - 03:24:41.0 112 1 J1925+2106 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [CALIBRATE_PHASE.UNSPECIFIED]
03:24:47.0 - 03:26:11.0 113 2 G55.7+3.4 25064 [0,1,2,3,4,5,6,7] [9.37, 9.37, 9.37, 9.37, 9.37, 9.37, 9.37, 9.37] [OBSERVE_TARGET.UNSPECIFIED]
03:26:11.0 - 03:27:41.0 114 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
03:27:41.0 - 03:29:11.0 115 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
03:29:11.0 - 03:30:41.0 116 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
03:30:41.0 - 03:32:10.0 117 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
03:32:10.0 - 03:33:40.0 118 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
03:33:40.0 - 03:35:10.0 119 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
03:35:10.0 - 03:36:40.0 120 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
03:36:40.0 - 03:38:09.0 121 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
03:38:09.0 - 03:39:39.0 122 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
03:39:39.0 - 03:41:09.0 123 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
03:41:09.0 - 03:42:39.0 124 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
03:42:39.0 - 03:44:08.0 125 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
03:44:08.0 - 03:45:38.0 126 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
03:45:38.0 - 03:47:08.0 127 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
03:47:08.0 - 03:48:38.0 128 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
03:48:38.0 - 03:50:07.0 129 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
03:50:07.0 - 03:51:37.0 130 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
03:51:37.0 - 03:53:07.0 131 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
03:53:07.0 - 03:54:36.0 132 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
03:54:43.0 - 03:56:07.0 133 1 J1925+2106 25064 [0,1,2,3,4,5,6,7] [9.38, 9.38, 9.38, 9.38, 9.38, 9.38, 9.38, 9.38] [CALIBRATE_PHASE.UNSPECIFIED]
03:56:07.0 - 03:57:36.0 134 1 J1925+2106 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [CALIBRATE_PHASE.UNSPECIFIED]
03:57:36.0 - 03:59:05.0 135 1 J1925+2106 24440 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [CALIBRATE_PHASE.UNSPECIFIED]
03:59:12.0 - 04:00:36.0 136 2 G55.7+3.4 25064 [0,1,2,3,4,5,6,7] [9.38, 9.38, 9.38, 9.38, 9.38, 9.38, 9.38, 9.38] [OBSERVE_TARGET.UNSPECIFIED]
04:00:36.0 - 04:02:06.0 137 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
04:02:06.0 - 04:03:35.0 138 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
04:03:35.0 - 04:05:05.0 139 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
04:05:05.0 - 04:06:35.0 140 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
04:06:35.0 - 04:08:05.0 141 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
04:08:05.0 - 04:09:34.0 142 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
04:09:34.0 - 04:11:04.0 143 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
04:11:04.0 - 04:12:34.0 144 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
04:12:34.0 - 04:14:04.0 145 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
04:14:04.0 - 04:15:33.0 146 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
04:15:33.0 - 04:17:03.0 147 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
04:17:03.0 - 04:18:33.0 148 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
04:18:33.0 - 04:20:03.0 149 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
04:20:03.0 - 04:21:32.0 150 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
04:21:32.0 - 04:23:02.0 151 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
04:23:02.0 - 04:24:32.0 152 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
04:24:32.0 - 04:26:02.0 153 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
04:26:02.0 - 04:27:31.0 154 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
04:27:31.0 - 04:29:00.0 155 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
04:29:10.0 - 04:30:31.0 156 1 J1925+2106 22512 [0,1,2,3,4,5,6,7] [9.98, 9.98, 9.98, 9.98, 9.98, 9.98, 9.98, 9.98] [CALIBRATE_PHASE.UNSPECIFIED]
04:30:31.0 - 04:32:01.0 157 1 J1925+2106 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_PHASE.UNSPECIFIED]
04:32:01.0 - 04:33:30.0 158 1 J1925+2106 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [CALIBRATE_PHASE.UNSPECIFIED]
04:33:40.0 - 04:35:00.0 159 2 G55.7+3.4 22464 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
04:35:00.0 - 04:36:30.0 160 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
04:36:30.0 - 04:38:00.0 161 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
04:38:00.0 - 04:39:29.0 162 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
04:39:29.0 - 04:40:59.0 163 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
04:40:59.0 - 04:42:29.0 164 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
04:42:29.0 - 04:43:59.0 165 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
04:43:59.0 - 04:45:28.0 166 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
04:45:28.0 - 04:46:58.0 167 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
04:46:58.0 - 04:48:28.0 168 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
04:48:28.0 - 04:49:58.0 169 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
04:49:58.0 - 04:51:27.0 170 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
04:51:27.0 - 04:52:57.0 171 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
04:52:57.0 - 04:54:27.0 172 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
04:54:27.0 - 04:55:57.0 173 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
04:55:57.0 - 04:57:26.0 174 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
04:57:26.0 - 04:58:56.0 175 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
04:58:56.0 - 05:00:26.0 176 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
05:00:26.0 - 05:01:56.0 177 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
05:01:56.0 - 05:03:25.0 178 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
05:03:35.0 - 05:04:55.0 179 1 J1925+2106 22464 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_PHASE.UNSPECIFIED]
05:04:55.0 - 05:06:25.0 180 1 J1925+2106 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_PHASE.UNSPECIFIED]
05:06:25.0 - 05:07:54.0 181 1 J1925+2106 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [CALIBRATE_PHASE.UNSPECIFIED]
05:08:04.0 - 05:09:24.0 182 2 G55.7+3.4 22752 [0,1,2,3,4,5,6,7] [9.76, 9.76, 9.76, 9.76, 9.76, 9.76, 9.76, 9.76] [OBSERVE_TARGET.UNSPECIFIED]
05:09:24.0 - 05:10:54.0 183 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
05:10:54.0 - 05:12:24.0 184 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
05:12:24.0 - 05:13:54.0 185 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
05:13:54.0 - 05:15:23.0 186 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
05:15:23.0 - 05:16:53.0 187 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
05:16:53.0 - 05:18:23.0 188 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
05:18:23.0 - 05:19:53.0 189 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
05:19:53.0 - 05:21:22.0 190 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
05:21:22.0 - 05:22:52.0 191 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
05:22:52.0 - 05:24:22.0 192 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
05:24:22.0 - 05:25:52.0 193 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
05:25:52.0 - 05:27:22.0 194 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
05:27:22.0 - 05:28:51.0 195 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
05:28:51.0 - 05:30:21.0 196 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
05:30:21.0 - 05:31:51.0 197 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
05:31:51.0 - 05:33:21.0 198 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
05:33:21.0 - 05:34:50.0 199 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
05:34:50.0 - 05:36:20.0 200 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
05:36:20.0 - 05:37:49.0 201 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
05:37:58.0 - 05:39:20.0 202 1 J1925+2106 25272 [0,1,2,3,4,5,6,7] [9.11, 9.11, 9.11, 9.11, 9.11, 9.11, 9.11, 9.11] [CALIBRATE_PHASE.UNSPECIFIED]
05:39:20.0 - 05:40:49.0 203 1 J1925+2106 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [CALIBRATE_PHASE.UNSPECIFIED]
05:40:49.0 - 05:42:18.0 204 1 J1925+2106 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [CALIBRATE_PHASE.UNSPECIFIED]
05:42:27.0 - 05:43:49.0 205 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.11, 9.11, 9.11, 9.11, 9.11, 9.11, 9.11, 9.11] [OBSERVE_TARGET.UNSPECIFIED]
05:43:49.0 - 05:45:19.0 206 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
05:45:19.0 - 05:46:48.0 207 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
05:46:48.0 - 05:48:18.0 208 2 G55.7+3.4 23400 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
05:48:18.0 - 05:49:48.0 209 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
05:49:48.0 - 05:51:18.0 210 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
05:51:18.0 - 05:52:47.0 211 2 G55.7+3.4 23400 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
05:52:47.0 - 05:54:17.0 212 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
05:54:17.0 - 05:55:47.0 213 2 G55.7+3.4 23608 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
05:55:47.0 - 05:57:17.0 214 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
05:57:17.0 - 05:58:46.0 215 2 G55.7+3.4 23400 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
05:58:46.0 - 06:00:16.0 216 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
06:00:16.0 - 06:01:46.0 217 2 G55.7+3.4 23400 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
06:01:46.0 - 06:03:16.0 218 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
06:03:16.0 - 06:04:45.0 219 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
06:04:45.0 - 06:06:15.0 220 2 G55.7+3.4 23608 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
06:06:15.0 - 06:07:45.0 221 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
06:07:45.0 - 06:09:15.0 222 2 G55.7+3.4 23400 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
06:09:15.0 - 06:10:44.0 223 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
06:10:44.0 - 06:12:13.0 224 2 G55.7+3.4 23608 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
06:12:21.0 - 06:13:44.0 225 1 J1925+2106 25272 [0,1,2,3,4,5,6,7] [9.22, 9.22, 9.22, 9.22, 9.22, 9.22, 9.22, 9.22] [CALIBRATE_PHASE.UNSPECIFIED]
06:13:44.0 - 06:15:14.0 226 1 J1925+2106 24856 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_PHASE.UNSPECIFIED]
06:15:14.0 - 06:16:43.0 227 1 J1925+2106 23608 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [CALIBRATE_PHASE.UNSPECIFIED]
06:16:50.0 - 06:18:13.0 228 2 G55.7+3.4 23200 [0,1,2,3,4,5,6,7] [9.28, 9.28, 9.28, 9.28, 9.28, 9.28, 9.28, 9.28] [OBSERVE_TARGET.UNSPECIFIED]
06:18:13.0 - 06:19:43.0 229 2 G55.7+3.4 24640 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
06:19:43.0 - 06:21:13.0 230 2 G55.7+3.4 23400 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
06:21:13.0 - 06:22:42.0 231 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
06:22:42.0 - 06:24:12.0 232 2 G55.7+3.4 23608 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
06:24:12.0 - 06:25:42.0 233 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
06:25:42.0 - 06:27:12.0 234 2 G55.7+3.4 23400 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
06:27:12.0 - 06:28:41.0 235 2 G55.7+3.4 23400 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
06:28:41.0 - 06:30:11.0 236 2 G55.7+3.4 23600 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
06:30:11.0 - 06:31:41.0 237 2 G55.7+3.4 23400 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
06:31:41.0 - 06:33:11.0 238 2 G55.7+3.4 23600 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
06:33:11.0 - 06:34:40.0 239 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
06:34:40.0 - 06:36:10.0 240 2 G55.7+3.4 23608 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
06:36:10.0 - 06:37:40.0 241 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
06:37:40.0 - 06:39:10.0 242 2 G55.7+3.4 23400 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
06:39:10.0 - 06:40:39.0 243 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
06:40:39.0 - 06:42:09.0 244 2 G55.7+3.4 22008 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
06:42:09.0 - 06:43:39.0 245 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
06:43:39.0 - 06:45:09.0 246 2 G55.7+3.4 21800 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
06:45:09.0 - 06:46:38.0 247 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
06:46:45.0 - 06:48:08.0 248 1 J1925+2106 23632 [0,1,2,3,4,5,6,7] [9.27, 9.27, 9.27, 9.27, 9.27, 9.27, 9.27, 9.27] [CALIBRATE_PHASE.UNSPECIFIED]
06:48:08.0 - 06:49:38.0 249 1 J1925+2106 24232 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_PHASE.UNSPECIFIED]
06:49:38.0 - 06:51:07.0 250 1 J1925+2106 25064 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [CALIBRATE_PHASE.UNSPECIFIED]
06:51:14.0 - 06:52:37.0 251 2 G55.7+3.4 24856 [0,1,2,3,4,5,6,7] [9.26, 9.26, 9.26, 9.26, 9.26, 9.26, 9.26, 9.26] [OBSERVE_TARGET.UNSPECIFIED]
06:52:37.0 - 06:54:07.0 252 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
06:54:07.0 - 06:55:37.0 253 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
06:55:37.0 - 06:57:07.0 254 2 G55.7+3.4 23400 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
06:57:07.0 - 06:58:37.0 255 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
06:58:37.0 - 07:00:06.0 256 2 G55.7+3.4 23400 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
07:00:06.0 - 07:01:36.0 257 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
07:01:36.0 - 07:03:06.0 258 2 G55.7+3.4 23400 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
07:03:06.0 - 07:04:36.0 259 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
07:04:36.0 - 07:06:05.0 260 2 G55.7+3.4 23400 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
07:06:05.0 - 07:07:35.0 261 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
07:07:35.0 - 07:09:05.0 262 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
07:09:05.0 - 07:10:35.0 263 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
07:10:35.0 - 07:12:04.0 264 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
07:12:04.0 - 07:13:34.0 265 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
07:13:34.0 - 07:15:04.0 266 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
07:15:04.0 - 07:16:34.0 267 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
07:16:34.0 - 07:18:03.0 268 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
07:18:03.0 - 07:19:33.0 269 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
07:19:33.0 - 07:21:02.0 270 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
07:21:09.0 - 07:22:33.0 271 1 J1925+2106 25064 [0,1,2,3,4,5,6,7] [9.38, 9.38, 9.38, 9.38, 9.38, 9.38, 9.38, 9.38] [CALIBRATE_PHASE.UNSPECIFIED]
07:22:33.0 - 07:24:02.0 272 1 J1925+2106 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [CALIBRATE_PHASE.UNSPECIFIED]
07:24:02.0 - 07:25:31.0 273 1 J1925+2106 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [CALIBRATE_PHASE.UNSPECIFIED]
07:25:38.0 - 07:27:02.0 274 2 G55.7+3.4 25064 [0,1,2,3,4,5,6,7] [9.38, 9.38, 9.38, 9.38, 9.38, 9.38, 9.38, 9.38] [OBSERVE_TARGET.UNSPECIFIED]
07:27:02.0 - 07:28:32.0 275 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
07:28:32.0 - 07:30:01.0 276 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
07:30:01.0 - 07:31:31.0 277 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
07:31:31.0 - 07:33:01.0 278 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
07:33:01.0 - 07:34:31.0 279 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
07:34:31.0 - 07:36:00.0 280 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
07:36:00.0 - 07:37:30.0 281 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
07:37:30.0 - 07:39:00.0 282 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
07:39:00.0 - 07:40:30.0 283 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
07:40:30.0 - 07:41:59.0 284 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
07:41:59.0 - 07:43:29.0 285 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
07:43:29.0 - 07:44:59.0 286 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
07:44:59.0 - 07:46:29.0 287 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
07:46:29.0 - 07:47:58.0 288 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
07:47:58.0 - 07:49:28.0 289 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
07:49:28.0 - 07:50:58.0 290 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
07:50:58.0 - 07:52:28.0 291 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
07:52:28.0 - 07:53:57.0 292 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
07:53:57.0 - 07:55:26.0 293 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
07:55:33.0 - 07:56:57.0 294 1 J1925+2106 25064 [0,1,2,3,4,5,6,7] [9.38, 9.38, 9.38, 9.38, 9.38, 9.38, 9.38, 9.38] [CALIBRATE_PHASE.UNSPECIFIED]
07:56:57.0 - 07:58:27.0 295 1 J1925+2106 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_PHASE.UNSPECIFIED]
07:58:27.0 - 07:59:56.0 296 1 J1925+2106 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [CALIBRATE_PHASE.UNSPECIFIED]
08:00:02.0 - 08:01:26.0 297 2 G55.7+3.4 25064 [0,1,2,3,4,5,6,7] [9.38, 9.38, 9.38, 9.38, 9.38, 9.38, 9.38, 9.38] [OBSERVE_TARGET.UNSPECIFIED]
08:01:26.0 - 08:02:56.0 298 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
08:02:56.0 - 08:04:26.0 299 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
08:04:26.0 - 08:05:55.0 300 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
08:05:55.0 - 08:07:25.0 301 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
08:07:25.0 - 08:08:55.0 302 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
08:08:55.0 - 08:10:25.0 303 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
08:10:25.0 - 08:11:54.0 304 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
08:11:54.0 - 08:13:24.0 305 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [OBSERVE_TARGET.UNSPECIFIED]
08:13:24.0 - 08:14:53.0 306 2 G55.7+3.4 25272 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [OBSERVE_TARGET.UNSPECIFIED]
08:17:47.0 - 08:17:54.0 308 3 0542+498=3C147 80 [0,1,2,3,4,5,6,7] [7, 7, 7, 7, 7, 7, 7, 7] [CALIBRATE_AMPLI.UNSPECIFIED,CALIBRATE_BANDPASS.UNSPECIFIED,UNSPECIFIED.UNSPECIFIED]
08:17:54.0 - 08:19:23.0 309 3 0542+498=3C147 17152 [0,1,2,3,4,5,6,7] [9.88, 9.88, 9.88, 9.88, 9.88, 9.88, 9.88, 9.88] [CALIBRATE_AMPLI.UNSPECIFIED,CALIBRATE_BANDPASS.UNSPECIFIED,UNSPECIFIED.UNSPECIFIED]
08:19:23.0 - 08:20:53.0 310 3 0542+498=3C147 18216 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_AMPLI.UNSPECIFIED,CALIBRATE_BANDPASS.UNSPECIFIED,UNSPECIFIED.UNSPECIFIED]
08:20:53.0 - 08:22:23.0 311 3 0542+498=3C147 18216 [0,1,2,3,4,5,6,7] [10, 10, 10, 10, 10, 10, 10, 10] [CALIBRATE_AMPLI.UNSPECIFIED,CALIBRATE_BANDPASS.UNSPECIFIED,UNSPECIFIED.UNSPECIFIED]
08:22:23.0 - 08:23:52.0 312 3 0542+498=3C147 18216 [0,1,2,3,4,5,6,7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [CALIBRATE_AMPLI.UNSPECIFIED,CALIBRATE_BANDPASS.UNSPECIFIED,UNSPECIFIED.UNSPECIFIED]
08:23:52.0 - 08:25:21.0 313 3 0542+498=3C147 18216 [0, 1, 2, 3, 4, 5, 6, 7] [9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89, 9.89] [CALIBRATE_AMPLI.UNSPECIFIED,CALIBRATE_BANDPASS.UNSPECIFIED,UNSPECIFIED.UNSPECIFIED]
(nRows = Total number of rows per scan)
Fields: 3
ID Code Name RA Decl Epoch SrcId nRows
1 D J1925+2106 19:25:59.605371 +21.06.26.16218 J2000 1 1004816
2 NONE G55.7+3.4 19:21:40.000000 +21.45.00.00000 J2000 2 6248936
3 N 0542+498=3C147 05:42:36.137916 +49.51.07.23356 J2000 3 90096
Spectral Windows: (8 unique spectral windows and 1 unique polarization setups)
SpwID Name #Chans Frame Ch0(MHz) ChanWid(kHz) TotBW(kHz) CtrFreq(MHz) Corrs
0 Subband:3 64 TOPO 1000.000 2000.000 128000.0 1063.0000 RR RL LR LL
1 Subband:1 64 TOPO 1128.000 2000.000 128000.0 1191.0000 RR RL LR LL
2 Subband:0 64 TOPO 1256.000 2000.000 128000.0 1319.0000 RR RL LR LL
3 Subband:2 64 TOPO 1384.000 2000.000 128000.0 1447.0000 RR RL LR LL
4 Subband:3 64 TOPO 1520.000 2000.000 128000.0 1583.0000 RR RL LR LL
5 Subband:1 64 TOPO 1648.000 2000.000 128000.0 1711.0000 RR RL LR LL
6 Subband:0 64 TOPO 1776.000 2000.000 128000.0 1839.0000 RR RL LR LL
7 Subband:2 64 TOPO 1904.000 2000.000 128000.0 1967.0000 RR RL LR LL
Sources: 24
ID Name SpwId RestFreq(MHz) SysVel(km/s)
1 J1925+2106 0 - -
1 J1925+2106 1 - -
1 J1925+2106 2 - -
1 J1925+2106 3 - -
1 J1925+2106 4 - -
1 J1925+2106 5 - -
1 J1925+2106 6 - -
1 J1925+2106 7 - -
2 G55.7+3.4 0 - -
2 G55.7+3.4 1 - -
2 G55.7+3.4 2 - -
2 G55.7+3.4 3 - -
2 G55.7+3.4 4 - -
2 G55.7+3.4 5 - -
2 G55.7+3.4 6 - -
2 G55.7+3.4 7 - -
3 0542+498=3C147 0 - -
3 0542+498=3C147 1 - -
3 0542+498=3C147 2 - -
3 0542+498=3C147 3 - -
3 0542+498=3C147 4 - -
3 0542+498=3C147 5 - -
3 0542+498=3C147 6 - -
3 0542+498=3C147 7 - -
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 W09 25.0 m -107.37.25.2 +33.53.51.0 -521.9416 -332.7766 -1.2001 -1601710.017000 -5042006.925200 3554602.355600
1 ea02 E02 25.0 m -107.37.04.4 +33.54.01.1 9.8240 -20.4293 -2.7806 -1601150.060300 -5042000.619800 3554860.729400
2 ea03 E09 25.0 m -107.36.45.1 +33.53.53.6 506.0564 -251.8670 -3.5825 -1600715.950800 -5042273.187000 3554668.184500
3 ea04 W01 25.0 m -107.37.05.9 +33.54.00.5 -27.3562 -41.3030 -2.7418 -1601189.030140 -5042000.493300 3554843.425700
4 ea05 W08 25.0 m -107.37.21.6 +33.53.53.0 -432.1167 -272.1478 -1.5054 -1601614.091000 -5042001.652900 3554652.509300
5 ea06 N06 25.0 m -107.37.06.9 +33.54.10.3 -54.0649 263.8778 -4.2273 -1601162.591000 -5041828.999000 3555095.896400
6 ea07 E05 25.0 m -107.36.58.4 +33.53.58.8 164.9788 -92.8032 -2.5268 -1601014.462000 -5042086.252000 3554800.799800
7 ea08 N01 25.0 m -107.37.06.0 +33.54.01.8 -30.8810 -1.4664 -2.8597 -1601185.634945 -5041978.156586 3554876.424700
8 ea09 E06 25.0 m -107.36.55.6 +33.53.57.7 236.9058 -126.3369 -2.4443 -1600951.588000 -5042125.911000 3554773.012300
9 ea10 N03 25.0 m -107.37.06.3 +33.54.04.8 -39.0773 93.0192 -3.3330 -1601177.376760 -5041925.073200 3554954.584100
10 ea11 E04 25.0 m -107.37.00.8 +33.53.59.7 102.8054 -63.7682 -2.6414 -1601068.790300 -5042051.910200 3554824.835300
11 ea12 E08 25.0 m -107.36.48.9 +33.53.55.1 407.8285 -206.0065 -3.2272 -1600801.926000 -5042219.366500 3554706.448200
12 ea13 N07 25.0 m -107.37.07.2 +33.54.12.9 -61.1037 344.2331 -4.6138 -1601155.635800 -5041783.843800 3555162.374100
13 ea15 W06 25.0 m -107.37.15.6 +33.53.56.4 -275.8288 -166.7451 -2.0590 -1601447.198000 -5041992.502500 3554739.687600
14 ea16 W02 25.0 m -107.37.07.5 +33.54.00.9 -67.9687 -26.5614 -2.7175 -1601225.255200 -5041980.383590 3554855.675000
15 ea17 W07 25.0 m -107.37.18.4 +33.53.54.8 -349.9877 -216.7509 -1.7975 -1601526.387300 -5041996.840100 3554698.327400
16 ea18 N09 25.0 m -107.37.07.8 +33.54.19.0 -77.4346 530.6273 -5.5859 -1601139.485100 -5041679.036800 3555316.533200
17 ea19 W04 25.0 m -107.37.10.8 +33.53.59.1 -152.8599 -83.8054 -2.4614 -1601315.893000 -5041985.320170 3554808.304600
18 ea20 N05 25.0 m -107.37.06.7 +33.54.08.0 -47.8454 192.6015 -3.8723 -1601168.786100 -5041869.054000 3555036.936000
19 ea21 E01 25.0 m -107.37.05.7 +33.53.59.2 -23.8638 -81.1510 -2.5851 -1601192.467800 -5042022.856800 3554810.438800
20 ea22 N04 25.0 m -107.37.06.5 +33.54.06.1 -42.6239 132.8436 -3.5494 -1601173.979400 -5041902.657700 3554987.517500
21 ea23 E07 25.0 m -107.36.52.4 +33.53.56.5 318.0509 -164.1850 -2.6957 -1600880.571400 -5042170.388000 3554741.457400
22 ea24 W05 25.0 m -107.37.13.0 +33.53.57.8 -210.0959 -122.3887 -2.2577 -1601377.009500 -5041988.665500 3554776.393400
23 ea25 N02 25.0 m -107.37.06.2 +33.54.03.5 -35.6245 53.1806 -3.1345 -1601180.861480 -5041947.453400 3554921.628700
24 ea26 W03 25.0 m -107.37.08.9 +33.54.00.1 -105.3447 -51.7177 -2.6037 -1601265.153600 -5041982.533050 3554834.858400
25 ea27 E03 25.0 m -107.37.02.8 +33.54.00.5 50.6641 -39.4835 -2.7273 -1601114.365500 -5042023.151800 3554844.944000
26 ea28 N08 25.0 m -107.37.07.5 +33.54.15.8 -68.9057 433.1889 -5.0602 -1601147.940400 -5041733.837000 3555235.956000
##### End Task: listobs #####
##########################################
We can see that there are three sources in this observation:
- J1925+2106, field ID 1: the phase calibrator;
- G55.7+3.4, field ID 2: the supernova remnant;
- 0542+498=3C147, field ID 3: the flux and bandpass calibrator.
We can also see that these sources have associated "scan intents", which indicate their function in the observation. Note that you can select sources based on their intents in certain CASA tasks. The various scan intents in this dataset are:
- CALIBRATE_PHASE indicates that this is a scan to be used for gain calibration;
- OBSERVE_TARGET indicates that this is the science target;
- CALIBRATE_AMPLI indicates that this is to be used for flux calibration; and
- CALIBRATE_BANDPASS indicates that these scans are to be used for bandpass calibration.
Note that 3C147 is to be used for both flux and bandpass calibration.
We can see the antenna configuration for this observation using plotants:
# In CASA
plotants('G55.7+3.4_10s.ms')
This shows that antennas ea01, ea18, and ea03 were on the extreme ends of the west, north, and east arms, respectively. The antenna position diagram is particularly useful as a guide to help determine which antenna to use as the reference antenna later during calibration.
We may also inspect the raw data using plotms. To start with, let's look at a subset of scans on the supernova remnant:
# In CASA
plotms(vis='G55.7+3.4_10s.ms', scan='30,75,120,165,190,235,303',
antenna='ea24', xaxis='freq', yaxis='amp', coloraxis='spw',
iteraxis='scan', correlation='RR,LL', symbolshape='circle')
The coloraxis parameter indicates that a different color will be assigned to each spectral window, and the iteraxis parameter tells plotms to display a new plot for each scan. We have chosen only one antenna (ea24) and just the right and left circular polarizations (without the cross-hand terms) to reduce the amount of data in the selection. One can flip through these plots using the green arrows located at the bottom of the plotting GUI: the double-left arrow will display the very first plot in the set, the single left arrow will go back one plot, and the right arrows have similar behavior for moving forward in the set.
Flipping through the scans, it's clear that there is significant time- and frequency-variable RFI present in this observation. Since this is L-band data taken in the most compact EVLA configuration ("D"), this comes as no surprise. However, it also poses one of the greatest challenges for obtaining a good image.
In particular, we can see that two spectral windows (SPWs) are quite badly affected. To determine which these are, click in the "Mark Regions" tool at the bottom of the plotms GUI (the open box with a green "plus" sign), and use the mouse to select a few of the highest-amplitude points in each of these SPWs. Click on the "Locate" button (magnifying glass sign), and information associated with the selected points will be displayed in the logger window:
Frequency in [1.22177 1.27139] or [1.5762 1.65063], Amp in [23.1713 24.3056] or [59.6296 63.6806]: Scan=30 Field=G55.7+3.4[2] Time=2010/08/23/01:20:57.0 BL=ea12@E08 & ea24@W05[11&22] Spw=1 Chan=59 Freq=1.246 Corr=RR X=1.246 Y=23.5243 (38134/11/1526) Scan=30 Field=G55.7+3.4[2] Time=2010/08/23/01:21:07.0 BL=ea03@E09 & ea24@W05[2&22] Spw=1 Chan=59 Freq=1.246 Corr=RR X=1.246 Y=23.6116 (40310/12/374) Scan=30 Field=G55.7+3.4[2] Time=2010/08/23/01:21:07.0 BL=ea12@E08 & ea24@W05[11&22] Spw=1 Chan=59 Freq=1.246 Corr=RR X=1.246 Y=23.4432 (41462/12/1526) Scan=30 Field=G55.7+3.4[2] Time=2010/08/23/01:21:57.0 BL=ea03@E09 & ea24@W05[2&22] Spw=1 Chan=59 Freq=1.246 Corr=RR X=1.246 Y=23.7536 (56950/17/374) Scan=30 Field=G55.7+3.4[2] Time=2010/08/23/01:21:07.0 BL=ea12@E08 & ea24@W05[11&22] Spw=4 Chan=41 Freq=1.602 Corr=RR X=1.602 Y=61.9097 (131282/39/1490) Scan=30 Field=G55.7+3.4[2] Time=2010/08/23/01:21:17.0 BL=ea12@E08 & ea24@W05[11&22] Spw=4 Chan=41 Freq=1.602 Corr=RR X=1.602 Y=61.1769 (134610/40/1490) Scan=30 Field=G55.7+3.4[2] Time=2010/08/23/01:21:27.0 BL=ea12@E08 & ea24@W05[11&22] Spw=4 Chan=41 Freq=1.602 Corr=RR X=1.602 Y=60.1834 (137938/41/1490) Found 7 points (7 unflagged) among 239616 in 0.02s.
We can see that SPWs 1 and 4 are among the worst affected by RFI. (As an aside, note that the syntax for reporting a selected point's baseline is {antenna 1 name}@{pad 1 name} &{antenna 2 name}@{pad 2 name}[{antenna 1 index}&{antenna 2 index}].) At this point, feel free to play around a bit more with plotms; you might try experimenting with different axes for iteration (under the "Iter" left-hand tab), different data selection parameters (under "Data"), different axes ("Axes"), different averaging techniques (under "Data"), or different selections for the coloraxis (the "colorize" option under "Display").
A priori calibration and flagging
Before we proceed with further processing, we should check the observation log to see if there were any issues noted during the run that need to be addressed. The observing log file is linked to the archive web page for this observation (at far right; under "logs etc."). Looking at the log, we can see that antenna ea07 may need a position correction, and antennas ea06, ea17, ea20, and ea26 did not have L-band receivers installed at the time and should be flagged.
Antenna position correction
Correcting a known position error for an antenna is done with the task gencal. This is important, because the observed visibilities are a function of [math]\displaystyle{ u }[/math] and [math]\displaystyle{ v }[/math]. If an antenna's position is incorrect, then [math]\displaystyle{ u }[/math] and [math]\displaystyle{ v }[/math] will be calculated incorrectly, and there will be errors in any image derived from the data. Of course, the a priori position corrections may not completely account for all errors.
The gencal task will query the VLA Baseline Corrections database to determine what baseline corrections to apply to the dataset. If you wish to double-check this by hand, refer to the EVLA/VLA Baseline Corrections page.
# In CASA
gencal(vis='G55.7+3.4_10s.ms', caltable='G55.7+3.4_10s.pos',
caltype='antpos')
As reported by the CASA logger, gencal found a position correction for antenna ea07 of (x, y, z) = (0.0087, 0.0137, 0.000) and recorded this in our specified calibration table.
Gain curve and opacity correction
A decision has been made here to ignore both the corrections for atmospheric opacity and for the elevation-dependent telescope gain throughout this tutorial. These effects are very small (less than or about 1%) across the frequency range of this observation (1-2 GHz). At higher frequencies, these corrections may be important and the appropriate calibration tables can be computed using the task gencal. For an example of a tutorial which corrects for atmospheric opacity and the elevation-dependent gain see this [this tutorial.]
Flagging non-operational antennas
In addition to updating the position for antenna ea07, we have to flag antennas ea06, ea17, ea20, and ea26, since these did not have working L-band receivers at the time of observation. We do this with the task flagdata:
# In CASA
flagdata(vis='G55.7+3.4_10s.ms', mode='manual',
antenna='ea06,ea17,ea20,ea26')
Note that the first thing flagdata does is create a backup flag file, in this case named "flagdata_1". This flag file contains a copy of the flags present in the MS prior to the requested flagging operation, and can be found inside the <MS_name>.flagversions directory, along with any other backed up flag files. Since these flag files take up a fair amount of space (in this particular case, 230 MB), we won't me making them every time we run flagdata -- the automatic flag backup can be turned off by setting flagbackup=False. However, it's good to keep a record of the names of the backup files and the associated processing step, in case you wish to restore a previous version of the flags using the flagmanager task.
Flagging shadowed antennas and zero-amplitude data
Since this is the most compact EVLA configuration, there may be instances where one antenna blocks, or "shadows" another. Therefore, we will run flagdata to remove these data:
# In CASA
flagdata(vis='G55.7+3.4_10s.ms', mode='shadow',
flagbackup=False)
In this particular observation, there does not appear to be any data affected by shadowing, as can be seen in the logger report.
In addition, there may be times during which the correlator writes out pure zero-valued data. In order to remove this bad data, we run flagdata to remove any pure zeroes:
# In CASA
flagdata(vis='G55.7+3.4_10s.ms', mode='clip',
clipzeros=True, flagbackup=False)
Inspecting the logger output which is generated by flagdata shows that there is a very small quantity of zero-valued data (0.02%) present in this MS.
Note that the archive will automatically flag shadowed antennas as well as zero-valued data, if you request that online flags are applied.
Automatic RFI excision
Now, we move on to one of the most difficult parts of L-band, D-configuration data processing: excising the RFI. For the original reduction of this MS, flagging was done by hand and took several weeks. The resulting data are offered as an option for the imaging stage of this tutorial (because careful by-hand flagging does yield a better image); however, it's not always practical to undertake this endeavor, and often the "automatic" flagging provides a reasonable (and much less time-consuming) solution. Therefore, we will demonstrate the use of the automatic RFI excision tools currently available in CASA.
Hanning-smoothing data
Prior to flagging any data which is affect by strong RFI, one should Hanning-smooth the data to remove Gibbs ringing. This is done with the task hanningsmooth, which can either write a new, Hanning-smoothed MS or directly operate on the requested column of the input MS. To conserve space, we will request the latter. Note that if you wish to make your own "before" and "after" plots, you should make the first prior to running hanningsmooth, since you will be overwriting the non-Hanning-smoothed data in the process -- and this is not reversible.
# In CASA
plotms(vis='G55.7+3.4_10s.ms', scan='30', antenna='ea24', spw='0~2',
xaxis='freq', yaxis='amp', coloraxis='spw', symbolshape = 'circle',
correlation='RR,LL', plotrange=[1.0,1.27,-0.3,2.5],
plotfile='amp_v_freq.beforeHanning.png')
hanningsmooth(vis='G55.7+3.4_10s.ms', datacolumn='data')
plotms(vis='G55.7+3.4_10s.ms', scan='30', antenna='ea24', spw='0~3',
xaxis='freq', yaxis='amp', coloraxis='spw', symbolshape = 'circle',
correlation='RR,LL', plotrange=[1.0,1.27,-0.3,2.5],
plotfile='amp_v_freq.afterHanning.png')
Task hanningsmooth will take a few minutes to run. Note that the 2nd plotms command above contains a trivial change in the spw selection (trivial because the 4th spw is outside of the specified plotrange). This forces plotms to reload the plot since by default, plotms will not redraw a plot if the input parameters are unchanged. In this case, since the data column was changed between calls to plotms, a redraw is necessary. When using the GUI, you can simply check "force reload" in the bottom left corner of the side bar before clicking "Plot."
We can compare the Hanning-smoothed data with the raw data by plotting a subset of data to show the result of Hanning-smoothing (see plots to the left and right). As you can see, the smoothing has spread the single-channel RFI into three channels, but has also removed the effects of some of the worst RFI from a number of channels. Overall, this will improve our ability to flag RFI from the data and retain as much good data as possible.
Using the phase calibration source for preliminary bandpass calibration
In order to get the best possible result from the automatic RFI excision, we will first apply bandpass calibration to the MS. Since the RFI is time-variable, using the phase calibration source to make an average bandpass over the entire observation will mitigate the amount of RFI present in the calculated bandpass. (For the final calibration, we will use the designated bandpass source 3C147; however, since this object was only observed in the last set of scans, it doesn't sample the time variability and would not provide a good average bandpass.)
Since there are likely to be gain variations over the course of the observation, we will run gaincal to solve for an initial set of antenna-based phases over a narrow range of channels. These will be used to create the bandpass solutions. While amplitude variations will have little effect on the bandpass solutions, it is important to solve for these phase variations with sufficient time resolution to prevent decorrelation when vector averaging the data in computing the bandpass solutions.
In order to choose a narrow range of channels for each spectral window which are relatively RFI-free over the course of the observation, we can look at the data with plotms. Note that it's important to only solve for phase using a narrow channel range, since an antenna-specific delay will cause the phase to vary with respect to frequency over the spectral window, perhaps by a substantial amount.
# In CASA
plotms(vis='G55.7+3.4_10s.ms', scan='30,75,120,165,190,235,303',
antenna='ea24', xaxis='channel', yaxis='amp', iteraxis='spw',
yselfscale=True, correlation='RR,LL', symbolshape='circle')
- yselfscale=True: sets the y-scaling to be for the currently displayed spectral window, since some spectral windows have much worse RFI and will skew the scale for others.
Looking at these plots, we can choose appropriate channel ranges for each SPW:
SPW 0: 10-13 SPW 1: 30-33 SPW 2: 32-35 SPW 3: 30-33 SPW 4: 35-38 SPW 5: 30-33 SPW 6: 30-33 SPW 7: 46-49
Using these channel ranges, we run gaincal to calculate phase-only solutions that will be used as input during our initial bandpass calibration. Remember - the calibration tables we are creating now are so that we can use automatic RFI flagging algorithms. Our final calibration tables will be generated later, after automated flagging. Here are the inputs for our initial pre-bandpass phase calibration:
# In CASA
gaincal(vis='G55.7+3.4_10s.ms', caltable='G55.7+3.4_10s.initPh',
intent='CALIBRATE_PHASE*', solint='int',
spw='0:10~13,1;3;5~6:30~33,2:32~35,4:35~38,7:46~49',
refant='ea24', minblperant=3,
minsnr=3.0, calmode='p', gaintable='G55.7+3.4_10s.pos')
- caltable='G55.7+3.4_10s.initPh': this is the output calibration table that will be written.
- intent='CALIBRATE_PHASE*': this is the way we have chosen to select data. Alternatively, we could have used "field='J1925+2106'", since this is the only source with the CALIBRATE_PHASE* scan intent. Note the use of the wildcard character "*" at the end of the string; this accounts for the fact that all the intents end with ".UNSPECIFIED". We could just as well have used "*PHASE*".
- solint='int': we request a solution for each 10-second integration.
- spw='0:10~13,1;3;5~6:30~33,2:32~35,4:35~38,7:46~49': note the syntax of this selection: a ":" is used to separate the SPW from channel selection, ";" is used to separate within this selection, and "~" is used to indicate an inclusive range.
- refant='ea24': we have chosen ea24 as the reference antenna after inspecting the antenna position diagram (see above). It is relatively close to, but not directly in, the center of the array, which could be important in D-configuration, since you don't want the reference antenna to have a high probability of being shadowed by nearby antennas.
- minblperant=3: the minimum number of baselines which must be present to attempt a phase solution.
- minsnr=3.0: the minimum signal-to-noise a solution must have to be considered acceptable. Note that solutions which fail this test will cause these data to be flagged downstream of this calibration step.
- calmode='p': perform phase-only solutions.
- gaintable='G55.7+3.4_10s.pos': use the antenna position correction for ea07 that we created earlier.
Note that a number of solutions do not pass the requirements of the minimum 3 baselines (generating the terminal message "Insufficient unflagged antennas to proceed with this solve.") or minimum signal-to-noise ratio (outputting "n of x solutions rejected due to SNR < 3 ..."). A particularly large number of solutions are rejected in SPW 4, where the RFI is most severe.
We can inspect the resulting calibration table with plotcal:
# In CASA
plotcal(caltable='G55.7+3.4_10s.initPh', xaxis='time', yaxis='phase',
iteration='antenna', spw='0', plotrange=[-1,-1,-180,180])
This iterates over antenna for a single spectral window; we can see that the phase does not change much over the course of the observation for SPW 0. We may also iterate over spectral window for a subset of antennas:
# In CASA
plotcal(caltable='G55.7+3.4_10s.initPh', xaxis='time', yaxis='phase',
iteration='spw', antenna='ea01,ea05,ea24', plotrange=[-1,-1,-180,180])
Clearly, the phases are affected by RFI in some places, especially in SPW 4.
Using this phase information, we create time-averaged bandpass solutions for the phase calibration source:
# In CASA
bandpass(vis='G55.7+3.4_10s.ms', caltable='G55.7+3.4_10s.initBP',
intent='CALIBRATE_PHASE*', solint='inf',
combine='scan', refant='ea24', minblperant=3, minsnr=10.0,
gaintable=['G55.7+3.4_10s.pos', 'G55.7+3.4_10s.initPh'],
interp=['', 'nearest'], solnorm=False)
- solint='inf', combine='scan': the solution interval of 'inf' will automatically break by scans; this requests that the solution intervals be combined over scans, so that we will get one solution per antenna.
- gaintable=['G55.7+3.4_10s.pos', 'G55.7+3.4_10s.initPh']: we will pre-apply both the antenna position corrections as well as the initial phase solutions.
- interp=['', 'nearest']: by default, gaincal will use linear interpolation for pre-applied calibration. However, we want the nearest phase solution to be used for a given time.
Again, we can see that a number of solutions have been rejected by our choices of minblperant and minsnr.
We may plot the bandpasses with plotcal; first looking at the amplitudes:
# In CASA
plotcal(caltable='G55.7+3.4_10s.initBP', xaxis='freq', yaxis='amp',
iteration='antenna', subplot=331)
- subplot=331: displays 3x3 plots per screen
Also, we can look at the phase solutions:
# In CASA
plotcal(caltable='G55.7+3.4_10s.initBP', xaxis='freq', yaxis='phase',
iteration='antenna', subplot=331)
We can see that SPW 4 is virtually wiped-out by RFI; furthermore, there are channels in SPW 1 that are consistently badly affected. Prior to running any automatic flagging, we will flag these manually. In addition, we will flag the first 9 channels of SPW 0, since this is affected by an issue which causes the noise to be substantially higher:
# In CASA
flagdata(vis='G55.7+3.4_10s.ms', spw='0:0~8,1:41~63,4')
Note that this has created a backup flag file called "flagdata_2". Now we apply the antenna position corrections and the bandpass calibration table to the data:
# In CASA
applycal(vis='G55.7+3.4_10s.ms',
gaintable=['G55.7+3.4_10s.pos', 'G55.7+3.4_10s.initBP'],
calwt=False)
