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We introduce the first AI-based framework for deep, super-resolution,wide-field radio-interferometric imaging, and demonstrate it on observations ofthe ESO~137-006 radio galaxy. The algorithmic framework to solve the inverseproblem for image reconstruction builds on a recent ``plug-and-play'' schemewhereby a denoising operator is injected as an image regulariser in anoptimisation algorithm, which alternates until convergence between denoisingsteps and gradient-descent data-fidelity steps. We investigate handcrafted andlearned variants of high-resolution high-dynamic range denoisers. We propose aparallel algorithm implementation relying on automated decompositions of theimage into facets and the measurement operator into sparse low-dimensionalblocks, enabling scalability to large data and image dimensions. We validateour framework for image formation at a wide field of view containingESO~137-006, from 19 gigabytes of MeerKAT data at 1053 and 1399 MHz. Therecovered maps exhibit significantly more resolution and dynamic range thanCLEAN, revealing collimated synchrotron threads close to the galactic core.

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