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. Task mossdi can be very slow for all but very extended emission, and its results can be poor. 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 options=double.
If you are familiar with the inputs to the conventional deconvolvers,
the inputs to mosmem
and mossdi
should be fairly
straightforward. In the case of the inputs to mosmem
and maxen
,
apart from differences in the options, the meaning of the flux
keyword and the default region, the only significant difference is in
specifying
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
fudge factor, rmsfac. That is, if rmsfac is
set at 1
(the default), then mosmem
uses the theoretical
noise level when calculating its 
statistic.
Typical inputs to mosmem are:
The inputs and use of mossdi should be equally simple for someone familiar with clean . Given that the task is not recommended, it will not be discussed further.
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 20.6.5 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:
