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. You'll also 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, so that clean will fit a line to each source's spectrum, as measured at 1300 and 1800 MHz. 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 be sure to set interactive=False.