Neural Network Analysis of S2-Star Dynamics: Extended mass
arXiv:2403.09748 · doi:10.1140/epjp/s13360-024-05042-0
Abstract
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 0.01% of the central mass inside the apocenter of S2. Neural networks analysis thus confirm its efficiency in the analysis of the S-star dynamics.
6 pages, 3 figs; Eur Phys J Plus (in press)
References in corpus (8)
- An Update on Monitoring Stellar Orbits in the Galactic Center
- Apoastron Shift Constraints on Dark Matter Distribution at the Galactic Center
- A new laser-ranged satellite for General Relativity and space geodesy: I. An introduction to the LARES2 space experiment
- The cosmological constant derived via galaxy groups and clusters
- Estimating the Parameters of Extended Gravity Theories with the Schwarzschild Precession of S2 Star
- Testing the Galactic Centre potential with S-stars
- A new laser-ranged satellite for General Relativity and space geodesy: III. De Sitter effect and the LARES 2 space experiment
- Neural Network Analysis of S-Star Dynamics: Implications for Modified Gravity