Generalized Langevin equation with colored noise description of the stochastic oscillations of accretion disks
Tiberiu Harko, Chun Sing Leung, Gabriela Mocanu

TL;DR
This paper models the stochastic oscillations of relativistic accretion disks using a generalized Langevin equation with colored noise, revealing how memory effects influence disk dynamics and variability, with potential implications for understanding AGN variability.
Contribution
It introduces a novel approach using a generalized Langevin equation with colored noise to describe accretion disk oscillations, incorporating memory effects and analyzing their impact on disk behavior.
Findings
Memory effects alter disk response to external perturbations.
Vertical displacements and luminosities are explicitly computed for Schwarzschild and Kerr black holes.
The model suggests stochastic disk instabilities could explain AGN variability features.
Abstract
We consider a description of the stochastic oscillations of the general relativistic accretion disks around compact astrophysical objects interacting with their external medium based on a generalized Langevin equation with colored noise, which accounts for the general memory and retarded effects of the frictional force, and on the fluctuation-dissipation theorem. The presence of the memory effects influences the response of the disk to external random interactions, and modifies the dynamical behavior of the disk, as well as the energy dissipation processes. The generalized Langevin equation of the motion of the disk in the vertical direction is studied numerically, and the vertical displacements, velocities and luminosities of the stochastically perturbed disks are explicitly obtained for both the Schwarzschild and the Kerr cases. The Power Spectral Distribution (PSD) of the disk…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
