The two mosaic deconvolution tasks are mosmem, which implements a maximum-entropy-based deconvolution algorithm, and task mossdi, which uses a Steer, Dewdney & Ito (SDI) CLEAN algorithm. Generally mosmem is superior, although mossdi can be better for images containing point sources. Note that, although you can make mosaiced, multi-frequency synthesis images with invert (and, indeed, produce a mosaiced, spectral dirty beam), there is no mosaic equivalent to mfclean. In deconvolving a mosaiced, multi-frequency image you will have to tactically assume that the spectral index is 0. This should not be a problem - primary beam model errors are probably more significant than spectral errors in these deconvolutions.
If you are deconvolving, note the recommendations for invert's
imsize parameter, and the use of
If you are familiar with the inputs to the conventional deconvolvers,
the inputs to mosmem
should be fairly
straightforward. In the case of the inputs to mosmem
apart from differences in the
options, the meaning of the
keyword and the default
region, the only significant difference is in
the expected RMS noise level in the dirty image. Because the noise level
varies across the dirty image, mosmem
uses the theoretically
expected noise level (which it computes) times a user-specified
rmsfac. That is, if
set at 1 (the default), then mosmem
uses the theoretical
noise level when calculating its statistic.
Typical inputs to mosmem are:
|Dirty image produced by invert.
|An initial model estimate - generally unset.
|The image that the solution should tend
|towards - generally unset.
|The output dataset.
|Maximum number of iterations - default is 30.
|Region to deconvolve. The default is the entire image.
|Leave unset gives you the Gull measure.
|Estimate of the total flux - its best to give a value.
|RMS noise fudge factor. Default is 1.
|An initial estimate of the beams volume. Generally
|you can leave this unset.
|Generally leave unset, or
doflux to enforce the flux constraint.
The inputs and use of mossdi should be equally simple for someone familiar with clean. If you are deconvolving a mosaic of linear polarisation try the task moscsdi, it does a combined clean on the Stokes Q and U images that gives better results for extended emission.
Having produced a model, we generally want to convolve this with a Gaussian CLEAN beam and add in the deconvolution residuals. This is done by restor. The inputs and use of restor is identical to a conventional observation (restor is the only general task which is smart enough to recognise a mosaiced experiment directly). Task restor uses a constant CLEAN beam - it is not a function of position. The only caveat is that, when determining a default CLEAN beam, restor fits a Gaussian to the synthesised beam which corresponds to the first pointing. Provided the first pointing is a fairly typical pointing, this will probably be adequate. Otherwise you may wish to use task mospsf (see Section 21.6 below) to generate an actual point-spread function (at some position) and then use imfit to determine Gaussian parameters for it.
Typical inputs to restor are:
|Dirty image produced by invert.
|Model produced by mosmem.
|Leave unset to get restored image.
|Beam size - leave unset to let restor
|fit it, but to the first pointing!
|Again leave unset to let restor fit it.