Detailed Information on Publication Record
2024
Explaining QPOs data for black holes in the Starobinsky-Bel-Robinson gravity
ABDULKHAMIDOV, Farukh, Bakhtiyor NARZILLOEV, Ibrar HUSSAIN, Ahmadjon ABDUJABBAROV, Bobomurat AHMEDOV et. al.Basic information
Original name
Explaining QPOs data for black holes in the Starobinsky-Bel-Robinson gravity
Authors
ABDULKHAMIDOV, Farukh (860 Uzbekistan, belonging to the institution), Bakhtiyor NARZILLOEV, Ibrar HUSSAIN, Ahmadjon ABDUJABBAROV (860 Uzbekistan) and Bobomurat AHMEDOV (860 Uzbekistan)
Edition
European Physical Journal C, New York (USA), SPRINGER, 2024, 1434-6044
Other information
Language
English
Type of outcome
Článek v odborném periodiku
Field of Study
10308 Astronomy
Country of publisher
United States of America
Confidentiality degree
není předmětem státního či obchodního tajemství
References:
Impact factor
Impact factor: 4.400 in 2022
Organization unit
Institute of physics in Opava
UT WoS
001207215100005
Keywords in English
quasi-periodic oscillations;charged particles;magnetic-field;circular orbits;motion;model;quintessence;precession;accration;signature
Tags
Tags
International impact, Reviewed
Links
GA23-07043S, research and development project.
Změněno: 4/2/2025 11:51, Mgr. Pavlína Jalůvková
Abstract
V originále
In this work we explore the properties of the spacetime around the Schwarzschild-like black hole in the Starobinsky–Bel–Robinson gravity through a test particle motion and its quasi periodic oscillations (QPOs) in the circular orbits in the close vicinity of the black hole horizon. We show how the extra spacetime parameter β affects the effective potential, energy, angular momentum, inner most circular orbit (ISCO) radius, and trajectory of a test particle. Next, we focus on the QPOs of particles and explore it in the relativistic precision model. Finally, we apply our analysis to explain the observational data obtained for several black hole candidates including GRO J1655-40, GRS 1915+105, XTE J1859+226, and XTE J1550-564 and get constraints on their spacetime parameters by employing the Markov Chain Monte Carlo analysis methodology.