[ Basic Info | References | User Guide ]
Basic Information on mfflat
Purpose: Flatten the spectral variation of a visibility data-set.
MFFLAT is a MIRIAD task which flattens the spectral variation of a
visibility data-set. This is used in multi-frequency synthesis,
where it is desirable to eliminate the dominant spectral variation
from the data (i.e. flatten the spectral variation), and so reduce
the spectral artifacts in the mapping and deconvolution process.
MFFLAT works by modifying the calibration tables of the input
Names of the input visibility data-sets. Several can be given.
Wildcard expansion is supported. On output, the calibration tables
of the data-set have been modified in such a way as to eliminate
the dominant spectral variation.
The input model. This must be MFS image, consisting of an intensity
and a scaled-derivative plane. This is analysed to determine the
dominant spectral index.