Персона: Галикян, Норайр Грачьяевич
Загружается...
Email Address
Birth Date
Научные группы
Организационные подразделения
Организационная единица
Институт ядерной физики и технологий
Цель ИЯФиТ и стратегия развития - создание и развитие научно-образовательного центра мирового уровня в области ядерной физики и технологий, радиационного материаловедения, физики элементарных частиц, астрофизики и космофизики.
Статус
Фамилия
Галикян
Имя
Норайр Грачьяевич
Имя
Результаты поиска
Теперь показываю 1 - 4 из 4
- ПубликацияТолько метаданныеS2-star dynamics probing the galaxy core cluster(2024) Galikyan, N.; Khlghatyan, S. h.; Kocharyan, A. A.; Gurzadyan, V. G.; Галикян, Норайр Грачьяевич
- ПубликацияОткрытый доступKolmogorov analysis of JWST deep survey galaxies(2025) Galikyan, N.; Kocharyan, A. A.; Gurzadyan, V. G.; Галикян, Норайр Грачьяевич
- ПубликацияОткрытый доступNeural network analysis of S2-star dynamics: extended mass(2024) Galikyan, N.; Khlghatyan, S. h.; Kocharyan, A. A.; Gurzadyan, V. G.; Галикян, Норайр ГрачьяевичPhysics-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 holes 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 confirms its efficiency in the analysis of the S-star dynamics.
- ПубликацияОткрытый доступNeural network analysis of S-star dynamics: implications for modified gravity(2023) Galikyan, N.; Khlghatyan, S. h.; Kocharyan, A. A.; Gurzadyan, V. G.; Галикян, Норайр ГрачьяевичWe studied the dynamics of S-stars in the Galactic center using the physics-informed neural networks. The neural networks are considered for both, Keplerian and the General Relativity dynamics, the orbital parameters for stars S1, S2, S9, S13, S31, and S54 are obtained, and the regression problem is solved. It is shown that the neural network is able to detect the Schwarzschild precession for S2 star, while the regressed part revealed an additional precession. Attributing the latter to a possible contribution of a modified gravity, we obtain a constraint for the weak-field modified General Relativity involving the cosmological constant which also deals with the Hubble tension. Our analysis shows the efficiency of neural networks in revealing the S-star dynamics and the prospects upon the increase in the amount and the accuracy of the observational data.