First AI for Deep Super-resolution Wide-field Imaging in Radio Astronomy: Unveiling Structure in ESO 137-006

Arwa Dabbech, Matthieu Terris, Adrian Jackson, Mpati A. Ramatsoku, Oleg M. Smirnov, Yves Wiaux

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
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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 regularizer in an optimization 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 GB 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.
Original languageEnglish
Article numberL4
JournalAstrophysical Journal Letters
Volume939
Issue number1
Early online date26 Oct 2022
DOIs
Publication statusPublished - 1 Nov 2022

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