Considering Numerical Relativity Surrogate Models for AGN Channel Merger Dynamics
Abstract
Binary black hole mergers (BBH) are currently observed by the gravitational-wave detectors LIGO, Virgo, and KAGRA (LVK). A fraction of these mergers are likely to originate in active galactic nuclei (AGN), but the details of the dynamical processes driving AGN channel mergers remain poorly understood. Monte Carlo For AGN Channel Testing and Simulation (McFACTS) is an open-source, public software used to simulate AGN channel BBH formation and evolution with configurable options for exploring various AGN and Nuclear Star Cluster (NSC) parameter options. Within McFACTS, BBH mergers evolve through gas drag and dynamical interactions, ignoring relativistic effects which can influence merger remnant properties. Here we demonstrate in McFACTS the effect of handing off inspiralling BBH to a numerical relativity (NR) surrogate model, which more accurately predicts the masses, spins, and kick velocities of merger remnants. Compared to a simple analytic approach to calculating merger and remnant properties the NR surrogate model is significantly more accurate at predicting remnant spins, which improve χeff and χp calculations, and recoil kicks, parameters especially important for higher generation BBH mergers. Our findings show the NR surrogate significantly changes distributions of remnant spins and kick velocities, as well as showing that alternative methods should be used cautiously in future studies. We also outline some simple models which are computationally efficient and can reproduce much of the NR surrogate model results.
Related
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- Binary Ellis-Bronnikov wormholes in 3+1 numerical relativity: mergers, fly-bys and gravitational waves
BibTeX
@misc{ray2026considering,
title = {{Considering Numerical Relativity Surrogate Models for AGN Channel Merger Dynamics}},
author = {Shawn Ray and Keefe Mitman and Vera Delfavero and Emily McPike and Jake Postiglione and Harrison E. Cook and Kaila Nathaniel and Nicolas Posner and Barry McKernan and K. E. Saavik Ford and Richard O'Shaughnessy},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.37895},
eprint = {2610.37895},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.37895},
}
RIS
TY - UNPB
TI - Considering Numerical Relativity Surrogate Models for AGN Channel Merger Dynamics
AU - Shawn Ray
AU - Keefe Mitman
AU - Vera Delfavero
AU - Emily McPike
AU - Jake Postiglione
AU - Harrison E. Cook
AU - Kaila Nathaniel
AU - Nicolas Posner
AU - Barry McKernan
AU - K. E. Saavik Ford
AU - Richard O'Shaughnessy
PY - 2026
DA - 2026/10/27
PB - arXiv
DO - 10.48550/arxiv.2610.37895
UR - https://arxiv.org/abs/2610.37895
AB - Binary black hole mergers (BBH) are currently observed by the gravitational-wave detectors LIGO, Virgo, and KAGRA (LVK). A fraction of these mergers are likely to originate in active galactic nuclei (AGN), but the details of the dynamical processes driving AGN channel mergers remain poorly understood. Monte Carlo For AGN Channel Testing and Simulation (McFACTS) is an open-source, public software used to simulate AGN channel BBH formation and evolution with configurable options for exploring various AGN and Nuclear Star Cluster (NSC) parameter options. Within McFACTS, BBH mergers evolve through gas drag and dynamical interactions, ignoring relativistic effects which can influence merger remnant properties. Here we demonstrate in McFACTS the effect of handing off inspiralling BBH to a numerical relativity (NR) surrogate model, which more accurately predicts the masses, spins, and kick velocities of merger remnants. Compared to a simple analytic approach to calculating merger and remnant properties the NR surrogate model is significantly more accurate at predicting remnant spins, which improve χ_eff and χ_p calculations, and recoil kicks, parameters especially important for higher generation BBH mergers. Our findings show the NR surrogate significantly changes distributions of remnant spins and kick velocities, as well as showing that alternative methods should be used cautiously in future studies. We also outline some simple models which are computationally efficient and can reproduce much of the NR surrogate model results.
ER -