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

Autor:	Dabbech, Arwa, Terris, Matthieu, Jackson, Adrian, Ramatsoku, Mpati, Smirnov, Oleg M., Wiaux, Yves
Rok vydání:	2022
Předmět:	Astrophysics - Instrumentation and Methods for Astrophysics Astrophysics - Astrophysics of Galaxies Computer Science - Machine Learning Electrical Engineering and Systems Science - Image and Video Processing
Zdroj:	2022 ApJL 939 L4
Druh dokumentu:	Working Paper
DOI:	10.3847/2041-8213/ac98af
Popis:	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. Comment: accepted for publication in ApJL
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2207.11336 Zobrazit plný text záznamu View this record from Arxiv