Robust sparse image reconstruction of radio interferometric observations with purify

TL;DR

This paper evaluates advanced sparse image reconstruction algorithms, implemented in PURIFY, for radio interferometry, demonstrating improved image quality over traditional methods like CLEAN, especially for large-scale data from telescopes like VLA and ATCA.

Contribution

It introduces and assesses the use of the P-ADMM algorithm within PURIFY for radio interferometric imaging, showing advantages over conventional methods.

Findings

Kaiser-Bessel kernel performs as well as prolate spheroidal wave functions with computational savings.

PURIFY produces higher quality images than CLEAN on real VLA and ATCA data.

PURIFY reconstructions have additional advantages such as scalability and image fidelity.

Abstract

Next-generation radio interferometers, such as the Square Kilometre Array (SKA), will revolutionise our understanding of the universe through their unprecedented sensitivity and resolution. However, to realise these goals significant challenges in image and data processing need to be overcome. The standard methods in radio interferometry for reconstructing images, such as CLEAN, have served the community well over the last few decades and have survived largely because they are pragmatic. However, they produce reconstructed inter\-ferometric images that are limited in quality and scalability for big data. In this work we apply and evaluate alternative interferometric reconstruction methods that make use of state-of-the-art sparse image reconstruction algorithms motivated by compressive sensing, which have been implemented in the PURIFY software package. In particular, we implement and apply the proximal alternating direction method of multipliers (P-ADMM) algorithm presented in a recent article. First, we assess the impact of the interpolation kernel used to perform gridding and degridding on sparse image reconstruction. We find that the Kaiser-Bessel interpolation kernel performs as well as prolate spheroidal wave functions, while providing a computational saving and an analytic form. Second, we apply PURIFY to real interferometric observations from the Very Large Array (VLA) and the Australia Telescope Compact Array (ATCA) and find images recovered by PURIFY are higher quality than those recovered by CLEAN. Third, we discuss how PURIFY reconstructions exhibit additional advantages over those recovered by CLEAN. The latest version of PURIFY, with developments presented in this work, is made publicly available.