Documentation on mfflat

Task: mfflat
Purpose: Flatten the spectral variation of a visibility data-set.
Categories: calibration

        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
        data-sets.

Key: vis
        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.

Key: model
        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.