First AI for deep super-resolution wide-field imaging in radio astronomy: unveiling structure in ESO 137--006

TL;DR

This paper presents the first AI-based deep super-resolution framework for wide-field radio-interferometric imaging, demonstrating improved resolution and dynamic range in images of ESO 137-006 from large MeerKAT data.

Contribution

It introduces a novel AI-driven inverse problem solution using plug-and-play denoisers for high-resolution radio imaging, scalable to large datasets.

Findings

Enhanced resolution and dynamic range over traditional methods.

Successful application to large MeerKAT data of ESO 137-006.

Revealed detailed structures near the galactic core.

Abstract

We introduce the first AI-based framework for deep, super-resolution, wide-field radio-interferometric imaging, and demonstrate it on observations of the ESO~137-006 radio galaxy. The algorithmic framework to solve the inverse problem for image reconstruction builds on a recent ``plug-and-play'' scheme whereby a denoising operator is injected as an image regulariser in an optimisation algorithm, which alternates until convergence between denoising steps and gradient-descent data-fidelity steps. We investigate handcrafted and learned variants of high-resolution high-dynamic range denoisers. We propose a parallel algorithm implementation relying on automated decompositions of the image into facets and the measurement operator into sparse low-dimensional blocks, enabling scalability to large data and image dimensions. We validate our framework for image formation at a wide field of view containing ESO~137-006, from 19 gigabytes of MeerKAT data at 1053 and 1399 MHz. The recovered maps exhibit significantly more resolution and dynamic range than CLEAN, revealing collimated synchrotron threads close to the galactic core.