Interferometric Decomposition — Code Review

[lucatelli]

Check the interferometric decomposition implementation.

• CASA task imsmooth is providing different results than scipy.signal.fftconvolve, setting proper normalisation parameters
• Check where CASA task imsmooth should or should not conserve flux density.
• Not adding a background estimate for complicated structures during minimisation is leaving significant negative residuals on VLA maps.
• Investigate scaling factors between different PSF beams.

[lucatelli]

Below, I report some comparisons between convolution using imsmooth and SciPy.

For this, two images were used:

• An image with a smaller restoring beam
• An image with a larger restoring beam.

The scaling factor applied to the SciPy convolution is:
scalling_factor = larger_beam_area/smaller_beam_area
Still, that result is not exactly the same as imsmooth. Why?

In the scaled case, the PSF is not normalised, and I assume that it is true for imsmooth.

The major differences comes with the total flux dentisy:

• Flux density image smaller beam = 23.85 mJy
• Flux density image smaller beam = 35.47 mJy
• Flux density convolved imsmooth = 27.02 mJy
• Flux density convolved scaled SciPy = 30.79 mJy