Combining Mosaics and Single Dish Data

For a good discussion of the theory and practice of combining single dish and mosaic data, see the paper by Snezana Stanimirovic (ASP Conf. Series, vol 278, p 375).

One of the benefits of mosaicing is that it partially recovers spacings shorter than the shortest projected interferometer spacing. In principle, the shortest spacing present in a mosaic can be the projected interferometer spacing minus the antenna diameter, D (see the references mentioned earlier for the argument). When antennas are as closely packed as is physically possible, the minimum physical spacing will be D, and so in principle a mosaic can reduce the effective minimum spacing almost down to the zero spacing. In practise, mosaicing tends to reduce the effective minimum spacing by about D/2, rather than the theoretical D. For the ATCA observations of sources appreciable far south, the effective minimum spacing in a mosaic is about 20 m (the minimum physical interferometer spacing is 31 m).

So whereas mosaicing helps recover short spacings, there are invariably some short spacing missing. To fill these spacings, interferometer data must be augmented with single-dish data. Just as there are two approaches to mosaicing (the joint and individual approach), Miriad provides two approaches to single-dish combination - the linear and a non-linear methods, using tasks immerge and mosmem respectively. Which method produces best results is quite problem specific, and indeed it is perhaps best to try both if possible. However,

Given that combining mosaic and single-dish data can be a bit of an art, its worth doing a few checks of the result:

In general, to combine single dish and interferometer data, you need to image the complete region containing emission. In practise this means that sidelobes, from emission outside the region of interest, are not significant. This applies both to the single-dish and interferometric observations. Both observations should map the complete region of the emission.

Miriad manager