arXiv subjects

N. Galikyan

Publications and source records attributed to N. Galikyan.

5 recordsLinked to original sources

arXiv2026

Kolmogorov analysis of pulsar TOA

The Kolmogorov stochasticity parameter (KSP) as a sensitive descriptor of the degree of randomness of signals was used to analyze the properties of the NANOGrav pulsar timing data associated with a stochastic gravitational wave background. The time of arrival (TOA) data of white noise for 68 pulsars were analyzed regarding their KSP properties. The analysis enabled us to obtain the degree of randomness of the white noise for various pulsars and to reveal its inhomogeneity, i.e., pulsars with low and high randomness of the white noise. The time dependence of the randomness in the white noise was also studied, indicating the existence of nonstationary physical processes influencing the pulsar timing. The KSP is thus acting as an indicator for the degree of the agreement between the observations and the timing models and as a test in revealing the contribution of various physical processes in the stochastic background signal.

astro-ph.IM↗

arXiv2025

Kolmogorov analysis of JWST deep survey galaxies

JWST galaxy deep spectral surveys provide a unique opportunity to trace a broad range of evolutionary features of galaxies and the intergalactic medium given the huge distance the photons are propagating. We have analyzed the spectral data of JWST galaxies up to a redshift of around 7 using the Kolmogorov technique, which is an efficient tool for testing the tiny comparative randomness properties of cumulative signals, that is, for distinguishing the contributions of regular and stochastic sub-signals. Our aim is to determine if certain identical spectral features of galaxies have undergone any distortions or systematic evolution across a broad range of redshifts. Our results indicate a change in the spectral properties of the sample galaxies at around z \simeq 2.7 at over a 99% confidence level.

astro-ph.GA↗

arXiv2024

S2-star dynamics probing the Galaxy core cluster

The star cluster surrounding the supermassive black hole in the center of Milky Way is probed using the data on the S2 star. The value of precession found at the physics-informed neural networks (PINN) analysis of the S2 data is used to consider the role of the scattering of S2 star on stars of the cluster, described by a random force given by the Holtsmark distribution. The critical value for the star density of the core cluster for which the observed precession value by PINN lies inside 70% confidence interval (between 15% and 85% quantiles) around the median of precession due to scattering, is obtained as n_crit \approx 8.3 10^6 pc^-3, that is at higher star densities the perturbation of the orbit of S2 would exceed the observed one.

astro-ph.GA↗

arXiv2024

Neural Network Analysis of S2-Star Dynamics: Extended mass

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.

gr-qc↗

arXiv2023

Neural Network Analysis of S-Star Dynamics: Implications for Modified Gravity

We 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 of the amount and the accuracy of the observational data.

physics.gen-ph↗