Here we describe two ad hoc methods of combining single-dish and
mosaic images. To some extent, these are of historical
interest only - the linear and non-linear methods mentioned before
are to be preferred.
These alternative methods are not implemented as tasks within Miriad, so
the steps to implement them are more hands-on. For example, if needed, you
will want to correct the flux calibration factor manually before using either
of these (e.g. immerge
can do this for you).
Some examples of using these approaches are given
paper by Snezana Stanimirovic
(ASP Conf. Series, vol. 278, p. 375).
- An alternative combination technique using mosmem
use the single-dish image as the default image (default keyword)
rather than as a separate constraint. That is, you use just
the mosaic image and beam with the map and beam parameters,
and set the single-dish image as the default image. Strictly
the default image should be in units of Jy/pixel. However
is smart enough to divide the pixel values by the beam
volume (where necessary) to perform some sort of crude conversion from
Jy/beam to Jy/pixel. You can constrain the total flux of the resultant
model to agree with the single-dish total flux, by using options=doflux.
is smart enough to use the default image as an flux
estimate when the doflux option is used and when the flux
is left unset.
- You could form an image which is a simple weighted average
of the mosaic and single-dish dirty images (i.e. before any deconvolution),
and then pass this to mosmem
if it was a normal mosaic. In doing this, you are
trying to fool mosmem
to some extent, so there are a number of fudges that you should do
to make the data better approximate what mosmem
Before adding the mosaic and single-dish images, you should apply
the residual primary beam attenuation present in the mosaic
to the single-dish data. Task mossen
will generate an
image of the residual primary beam attenuation (set the gain
Having generated a
the residual primary beam attenuation, you can then apply this to
the single-dish image using maths.
The weights used to add the mosaic and single-dish
images should sum to 1, but the actual way of determining the
relative weights is not well defined. One good choice, advocated by
Stanimirovic et al., is to weight the images in inverse proportional to the
beam volumes. When the difference in resolution between the mosaic
and single-dish observations is appreciable (i.e. the normal situation),
this weighting reduces to nearly the same as that performed by
immerge. Normalisation the weights to add to 1 gives
are the beam volumes of
the synthesised mosaic beam and the single-dish beam respectively).
To generate the effective beam dataset, you will also want to add
a component to the mosaic beam. Because of the process that
believes was used to generate a mosaic, the
component you add is not simply the single-dish beam - the mosaicing process
narrows down the width of the beams from that of individual
pointings. If both the interferometer primary beam and the single-dish beam
are gaussian forms with widths
respectively, then you will want to add a gaussian to the mosaic beam
dataset which has a width
This gaussian will be somewhat broader than
the single-dish resolution (for an ATCA and Parkes combination, it will be about
7% broader than the Parkes beam). You will want to add this gaussian and the
mosaic beam with the same weights that were used for the single-dish
and mosaic images.
Note that the mosaic beam dataset consists of one image per pointing, and that
you will want to add a gaussian to each plane.
can be used to do this,
although it warns you that its handling of cubes
With imgen, you can set the weighting by appropriately setting the
scaling factor (factor parameter) and the amplitude of the
gaussian (spar parameter).
Note that this technique ignores some edge effects.
Also it uses the theoretical noise level derived for the
mosaic observation. This will be close to what you want if the noise
levels for all pointings are about the same.