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

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...

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Bibliographic Details
Published in:Astrophysical journal. Letters 2022-11, Vol.939 (1), p.L4
Main Authors: Dabbech, A., Terris, M., Jackson, A., Ramatsoku, M., Smirnov, O. M., Wiaux, Y.
Format: Article
Language:English
Subjects:
Algorithms
Aperture synthesis
Astronomy
Astronomy image processing
Computational astronomy
Convolutional neural networks
Dynamic range
Galaxies
Image reconstruction
Image resolution
Inverse problems
Noise reduction
Optimization
Radio astronomy
Radio galaxies
Synchrotrons
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Summary: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.
ISSN:2041-8205
2041-8213
DOI:10.3847/2041-8213/ac98af