Work

A deep learning approach to image analysis of M87⋆

Year: 2025

Type: article

Abstract: ABSTRACT Deep learning-based computer vision models have demonstrated superior performance on a wide variety of image-related tasks, including image classification and object recognition. This study explores the potential of deep learning techniques for analysing images of M87$^{\star }$, using both simulated and real observational data. We investigate the performance of various neural network architectures for parameter inference, multitask learning, and super-resolution imaging. This work serves as a preliminary step towards developing robust deep learning methods for interpreting black hole imagery and advancing our understanding of black hole physics.

Source: Monthly Notices of the Royal Astronomical Society

DOI: 10.1093/mnras/staf2095

Authors: Tao Tsui, Pavlos Protopapas, Cecilia Garraffo, Christian M Fromm, Yosuke Mizuno, Lindy Blackburn, Koushik Chatterjee, Razieh Emami, Sheperd S Doeleman

Institutions: Harvard University, Center for Astrophysics Harvard & Smithsonian, Goethe University Frankfurt, University of Würzburg, Max Planck Institute for Radio Astronomy, Shanghai Jiao Tong University

Language: English

Topic: Astrophysical Phenomena and Observations

Subfield: Astronomy and Astrophysics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,046

Funders: National Science Foundation, Smithsonian Institution, John Templeton Foundation, Gordon and Betty Moore Foundation, Harvard University, Deutsche Forschungsgemeinschaft, National Natural Science Foundation of China

Awards: G2141898060, FOR 5195: Relativistische Jets in Aktiven Galaxien, G5043980916, G7031700240, Studies of Accretion onto Black Holes, G930603713

OpenAlex ID: W7114778097

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Cites: 43

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