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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 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. The resulting algorithms were deployed to form images of 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.
|Number of pages||11|
|Journal||Astrophysical Journal Letters|
|Publication status||Published - 26 Oct 2022|
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Extreme-scale precision Imaging in Radio Astronomy (EIRA)
1/09/20 → 31/08/24