A neural-network surrogate for null geodesics enables faster black hole rendering with gravitational lensing, roughly 15x faster than an Euler-method baseline, but accuracy is measured against that same baseline.
A Parameter Study of the Electromagnetic Signatures of an Analytical Mini-Disk Model for Supermassive Binary Black Hole Systems
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
Supermassive black holes (SMBHs) are thought to be located at the centers of most galactic nuclei. When galaxies merge they form supermassive black hole binary (SMBHB) systems and these central SMBHs will also merge at later times, producing gravitational waves (GWs). Because galaxy mergers are likely gas-rich environments, SMBHBs are also potential sources of electromagnetic (EM) radiation. The EM signatures depend on gas dynamics, orbital dynamics, and radiation processes. The gas dynamics are governed by general relativistic magnetohydrodynamics (MHD) in a time-dependent spacetime. Numerically solving the MHD equations for a time-dependent binary spacetime is computationally expensive. Therefore, it is challenging to conduct a full exploration of the parameter space of these systems and the resulting EM signatures. We have developed an analytical accretion disk model for the mini-disks of an SMBHB system and produced images and light curves using a general relativistic ray-tracing code and a superimposed harmonic binary black hole metric. This analytical model greatly reduces the time and computational resources needed to explore these systems, while incorporating some key information from simulations. We present a parameter space exploration of the SMBHB system in which we have studied the dependence of the EM signatures on the spins of the black holes (BHs), the mass ratio, the accretion rate, the viewing angle, and the initial binary separation. Additionally, we study how the commonly used fast-light approximation affects the EM signatures and evaluate its validity in GRMHD simulations.
citation-role summary
citation-polarity summary
fields
gr-qc 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Learning Null Geodesics for Gravitational Lensing Rendering in General Relativity
A neural-network surrogate for null geodesics enables faster black hole rendering with gravitational lensing, roughly 15x faster than an Euler-method baseline, but accuracy is measured against that same baseline.