Difference between revisions of "Imaging an EVLA OSRO HI data set"
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* '''Y Axis''' = Amp
* '''Y Axis''' = Amp
The figure to the right shows the resulting plot in [[plotms]]. There is clearly some line emission around channel 190, and possibly some faint emission around channel
The figure to the right shows the resulting plot in [[plotms]]. There is clearly some line emission around channel 190, and possibly some faint emission around channel . Let's use channels 10--55 and 120--155 to fit the continuum.
Revision as of 20:33, 11 March 2010
This tutorial explains how to image an HI dataset acquired with the WIDAR0 correlator. It assumes that you've already calibrated your data as described in the calibration tutorial, and that you now have a split dataset with a single source of interest in it. In this example, the source is called 'Leo-2'; see the calibration tutorial for more details on this data set.
Flag Your Split Data
For a spectral line dataset like this one, you'll probably want to average in various ways to spot bad data. Averaging channels together can make bad baselines pop up!
Presently, doppler tracking is not performed online by the EVLA, so we have to correct for any velocity shifts now, in post-processing, with cvel.
# cvel :: regrid an MS to a new spectral window / channel structure or frame vis = 'leo2.ms' # Name of input measurement set outputvis = 'leo2_cvel.ms' # Name of output measurement set passall = False # Pass through (write to output MS) non-selected data with no change field = '' # Select field using field id(s) or field name(s) spw = '0' # Select spectral window/channels selectdata = False # Other data selection parameters mode = 'channel' # Regridding mode nchan = -1 # Number of channels in output spw (-1=all) start = 0 # first input channel to use width = 1 # Number of input channels to average interpolation = 'linear' # Spectral interpolation method phasecenter = '' # Image phase center: position or field index restfreq = '1420405751.786Hz' # rest frequency (see help) outframe = 'BARY' # Output frame (''=keep input frame) veltype = 'radio' # velocity definition hanning = False # Turn on Hanning smoothing of spectral channels async = False # If true the taskname must be started using cvel(...)
Cvel creates a new measurement set (outputvis= 'leo2_cvel.ms') for which, at each time, the spectrum has been shifted and channels regridded to keep the spectral line centered in the frame of your choice (here, we choose barycentric; outframe= 'BARY'). You'll also want to give cvel the rest frequency of your spectral line (restfreq= '1420405751.786Hz' for HI).
In preparation for subtracting the continuum, let us plot up the combined spectrum on our science field and identify some line-free channels. Open up a plotms GUI window and load the doppler-tracked measurement set containing your science target. You'll want to average over both time and baselines to get as much signal-to-noise as possible, hopefully revealing a nice 21 cm profile. In the Data tab of plotms, set the below averaging options:
(See Averaging data in plotms for more details on averaging options).
You'll also want to click on the Axes tab in the plotms window, and change the axes settings to:
The figure to the right shows the resulting plot in plotms. There is clearly some line emission around channel 190, and possibly some faint emission around channel 88. Let's use channels 10--55 and 120--155 to fit the continuum.