Comparison of classical and Bayesian imaging in radio interferometry

TL;DR

This paper introduces 'resolve', a Bayesian imaging algorithm for radio interferometry that addresses CLEAN's limitations, providing super-resolution, uncertainty quantification, and reduced dependence on human choices, demonstrated on VLA data of Cygnus A.

Contribution

The paper presents 'resolve', a novel Bayesian imaging method that overcomes CLEAN's shortcomings, offering super-resolution and uncertainty estimates in radio interferometric imaging.

Findings

resolve achieves super-resolution in radio images

It provides uncertainty quantification for flux estimates

Noise correction factors vary between 0.4 and 429

Abstract

CLEAN, the commonly employed imaging algorithm in radio interferometry, suffers from a number of shortcomings: in its basic version it does not have the concept of diffuse flux, and the common practice of convolving the CLEAN components with the CLEAN beam erases the potential for super-resolution; it does not output uncertainty information; it produces images with unphysical negative flux regions; and its results are highly dependent on the so-called weighting scheme as well as on any human choice of CLEAN masks to guiding the imaging. Here, we present the Bayesian imaging algorithm resolve which solves the above problems and naturally leads to super-resolution. We take a VLA observation of Cygnus~A at four different frequencies and image it with single-scale CLEAN, multi-scale CLEAN and resolve. Alongside the sky brightness distribution resolve estimates a baseline-dependent correction function for the noise budget, the Bayesian equivalent of weighting schemes. We report noise correction factors between 0.4 and 429. The enhancements achieved by resolve come at the cost of higher computational effort.