can be given multiple input model
images. The input models can be cubes for multi-channel data. These different
models represent some different aspect of the data. For example, the data
could contain multiple pointing centres from a mosaiced observation or multiple
spectral lines. The self-calibration
tasks assume that the gains are not dependent on these different aspects of
the data (e.g. the gains are independent of frequency and pointing
centre). All the information will be used in a simultaneous solution of the
The self-calibration tasks will generally determine the appropriate correspondence
between data and model (e.g. it will associate the data from
a particular pointing with a model with the same pointing).
There are, however, some caveats described below.