Fast algorithms to approximate the positiondependent point spread function responses in radio interferometric widefield imaging
Abstract
The desire for wide field of view, large fractional bandwidth, high sensitivity, high spectral and temporal resolution has driven radio interferometry to the point of big data revolution where the data are represented in at least three dimensions with an axis for spectral windows, baselines, sources, etc., where each axis has its own set of subdimensions. The cost associated with storing and handling these data is very large, and therefore several techniques to compress interferometric data and/or speed up processing have been investigated. Unfortunately, averagingbased methods for visibility data compression are detrimental to the data fidelity, since the point spread function (PSF) is positiondependent, that is, distorted and attenuated as a function of distance from the phase centre. The position dependence of the PSF becomes more severe, requiring more PSF computations for widefield imaging. Deconvolution algorithms must take the distortion into account in the major and minor cycles to properly subtract the PSF and recover the fidelity of the image. This approach is expensive in computation since at each deconvolution iteration a distorted PSF must be computed. We present two algorithms that approximate these positiondependent PSFs with fewer computations. The first algorithm approximates the positiondependent PSFs in the uvplane and the second algorithm approximates the positiondependent PSFs in the image plane. The proposed algorithms are validated using simulated data from the MeerKAT telescope.
 Publication:

Monthly Notices of the Royal Astronomical Society
 Pub Date:
 November 2020
 DOI:
 10.1093/mnras/staa2843
 arXiv:
 arXiv:2009.07010
 Bibcode:
 2020MNRAS.499..292A
 Keywords:

 instrumentation: interferometers;
 methods: data analysis;
 methods: numerical;
 techniques: interferometric;
 Astrophysics  Instrumentation and Methods for Astrophysics
 EPrint:
 13 PAGES