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When you are satisfied
with the deconvolution, restoration and self-calibration of
all the individual images, task linmos
can be
used to combine them in a linear mosaic. Usually you will just combine the
restored images (if you are going to do quantitative analysis on the
composite image, it may be best to do a deep CLEAN and use the same
restoring beam for all pointings). Although linmos
can interpolate
input images to align them, its algorithm, particularly for geometric
correction, is very poor, and so this
is strongly
discouraged. You should use invert
to make all the input images on the
same grid, by setting a common tangent point (offset keyword).
Task linmos
uses the same weighted sum of the input pointings as
the `joint approach' software (see Section 20.6.1). Normally
the expected rms noise in the image is determined from the images
themselves (image item rms). However if this item is missing,
or if you wish to override it to get a different weighting, you may enter
the expected rms noise via keyword rms. Also note that linmos
,
by default, fully correct for the primary beam attenuation even
when this excessively amplifies the noise. The taper option
can be used to reduce the correction at the edge of the field, and thus
avoid excessive noise amplification.
Task linmos
can also produce an image giving
the expected rms noise as a function of position, and a gain image --
see the help on the options keyword for these.
Typical inputs to linmos
are:
Next: Generating Dummy Visibility
Up: The Individual Approach
Previous: DeconvolutionRestoration and
Last generated by rsault@atnf.csiro.au on 16 Jan 1996