https://doi.org/10.1140/epjp/s13360-024-05042-0
Regular Article
Neural network analysis of S2-star dynamics: extended mass
1
National Research Nuclear University MEPhI, Moscow, Russia
2
Center for Cosmology and Astrophysics, Alikhanian National Laboratory and Yerevan State University, Yerevan, Armenia
3
School of Physics and Astronomy, Monash University, Clayton, Australia
4
SIA, Sapienza Universita di Roma, Rome, Italy
Received:
4
January
2024
Accepted:
26
February
2024
Published online:
13
March
2024
Physics-informed neural network (PINN) analysis of the dynamics of S-stars in the vicinity of the supermassive black hole in the Galactic center is performed within General Relativity treatment. The aim is to reveal the role of possible extended mass (dark matter) configuration in the dynamics of the S-stars, in addition to the dominating central black hole’s mass. The PINN training fails to detect the extended mass perturbation in the observational data for S2 star within the existing data accuracy, and the precession constraint indicates no signature of extended mass up to 
of the central mass inside the apocenter of S2. Neural networks analysis thus confirms its efficiency in the analysis of the S-star dynamics.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
