Notes on EVLA OSRO continuum data sets
Calibrating and imaging EVLA continuum data really isn't much different from the strategies we used with the VLA--so check out this tutorial. The one big difference is that we've increased the number of channels from one (in VLA continuum mode) to 128 (in OSRO mode 1). That means:
- You'll have to do a bandpass calibration, just as you would for a spectral line data set; and
- You'll have to tell clean to do multi-frequency synthesis imaging. Easy enough---the defaults in clean are set up for this:
# clean :: Invert and deconvolve images with selected algorithm vis = '' # Name of input visibility file imagename = [''] # Pre-name of output images outlierfile = '' # Text file with image names, sizes, centers for outliers field = '' # Field Name or id spw = '' # Spectral windows e.g. '0~3', '' is all selectdata = False # Other data selection parameters mode = 'mfs' # Spectral gridding type (mfs, channel, velocity, frequency) nterms = 1 # Number of terms used to model the sky frequency dependence reffreq = '' # Reference frequency for MFS (relevant only if nterms > 1) gridmode = '' # Gridding kernel for FFT-based transforms, default='' None niter = 500 # Maximum number of iterations gain = 0.1 # Loop gain for cleaning threshold = '0.0mJy' # Flux level to stop cleaning, must include units: '1.0mJy' psfmode = 'clark' # Method of PSF calculation to use during minor cycles imagermode = '' # Options: 'csclean' or 'mosaic', '', uses psfmode multiscale =  # Deconvolution scales (pixels);  = standard clean interactive = False # Use interactive clean (with GUI viewer) mask =  # Cleanbox(es), mask image(s), and/or mask region(s) imsize = [256, 256] # x and y image size in pixels. Single value: same for both cell = ['1.0arcsec'] # x & y cell size(s). Default unit arcsec. phasecenter = '' # Image center: direction or field index restfreq = '' # Rest frequency to assign to image (see help) stokes = 'I' # Stokes params to image (eg I,IV, QU,IQUV) weighting = 'natural' # Weighting of uv (natural, uniform, briggs, ...) uvtaper = False # Apply additional uv tapering of visibilities modelimage = '' # Name of model image(s) to initialize cleaning restoringbeam = [''] # Output Gaussian restoring beam for CLEAN image pbcor = False # Output primary beam-corrected image minpb = 0.2 # Minimum PB level to use calready = True # True required for self-calibration async = False # If true the taskname must be started using clean(...)
Setting mode= 'mfs' will use all channels in your data set to make an image. If you'd only like to use a subset of channels for the image, set spw.
The nterms and reffreq sub-parameters under mode= 'mfs' improve synthesis across wide fractional bandwidths. If nterms> 1, clean will fit a polynomial of order nterms to model flux density as a function of frequency for each source in the imaging field (using an algorithm developed by U. Rau). For example, imagine a data set with two spectral windows: one at 1300 MHz and one at 1800 MHz. The difference between these two frequencies is a significant fraction of the frequencies themselves, therefore implying that the sources' flux densities, the synthesized beam, and the primary beam will all vary significantly between the two spectral windows. In this case, imaging can be greatly improved by setting nterms=2 (or perhaps even higher), so that clean will fit a line to each source's spectrum. The image that is produced by clean will represent the sky at frequency reffreq---set this to be somewhere in the middle of the two observed frequencies (right now, clean seems to run more stably if reffreq is at the low end, closer to 1300 MHz in our example).
Warning: The implementation of multi-frequency synthesis imaging with nterms> 1 is still rather preliminary in clean, so use caution. It does not seem to work with interactive imaging at the moment, so you'll want to set your clean boxes before imaging and set interactive=False in clean.
Last checked on CASA Version 3.0.1 (r10803).
--Laura Chomiuk 21:29, 18 March 2010 (UTC)