Machine learning for interferometric image reconstruction with sparse arrays

Joel Sanchez-Bermudez; Alejandro Cruz-Osorio; Jorge K. Barrera-Ballesteros; Antxon Alberdi; Rainer Schödel

doi:10.1117/12.3020320

28 August 2024 Machine learning for interferometric image reconstruction with sparse arrays

Joel Sanchez-Bermudez, Alejandro Cruz-Osorio, Jorge K. Barrera-Ballesteros, Antxon Alberdi, Rainer Schödel

Proceedings Volume 13095, Optical and Infrared Interferometry and Imaging IX; 1309519 (2024) https://doi.org/10.1117/12.3020320
Event: SPIE Astronomical Telescopes + Instrumentation, 2024, Yokohama, Japan

Abstract

Interferometry, essential in radio and infrared astronomy, faces a significant challenge: reconstructing images from sparsely sampled data. Current regularized minimization algorithms rely heavily on predefined priors and hyperparameters, leading to ambiguities and inaccuracies in the images. Here, we present a project to integrate Neural Networks into interferometric image reconstruction. By utilizing the principles of Compressed Sensing and generative Neural Networks, this approach can map infrared interferometric data to reconstruct images more accurately, reducing reliance on rigid priors. The adaptability of the Neural Network ensures that the reconstructions are more precise and less dependent on user input, which is a significant advancement over current methods that require extensive expertise. In this work, we present, as software demonstration, reconstructions obtained from the Event Horizon Telescope data of the black-hole shadow at the core of M87.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Joel Sanchez-Bermudez, Alejandro Cruz-Osorio, Jorge K. Barrera-Ballesteros, Antxon Alberdi, and Rainer Schödel "Machine learning for interferometric image reconstruction with sparse arrays", Proc. SPIE 13095, Optical and Infrared Interferometry and Imaging IX, 1309519 (28 August 2024); https://doi.org/10.1117/12.3020320

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