Accurate models for recoil velocity distribution in black hole mergers with comparable to extreme mass-ratios and their astrophysical implications

TL;DR

This paper introduces two new models for accurately predicting the recoil velocity distribution in black hole mergers across a wide range of mass ratios, improving upon existing models and impacting astrophysical retention estimates.

Contribution

The paper presents two novel models, a Gaussian process regression and a normalizing-flow model, that accurately predict recoil velocities for black hole mergers with mass ratios up to 200, surpassing previous models.

Findings

Models outperform existing ones within their domains.

Accurate recoil velocity predictions affect black hole retention estimates.

Models are publicly available for community use.

Abstract

Modeling the remnant recoil velocity (kick) distribution from binary black hole mergers is crucial for understanding hierarchical mergers in active galactic nuclei or globular clusters. Existing analytic models often show large discrepancies with numerical relativity (NR) data, while data-driven models are limited to mass ratios of q<=8 (aligned spins) and q<=4 (precessing spins) and break down when extrapolated outside their training ranges. Using ~5000 of NR simulations from the SXS and RIT catalogs up to q=128 and ~100 black hole perturbation theory simulations up to q=200, we present two classes of models: (i) gwModel_kick_q200 (gwModel_kick_q200_GPR), an analytic (Gaussian process regression) model for aligned-spin binaries. (ii) gwModel_kick_prec_flow, a normalizing-flow model for kick distribution from precessing binaries with isotropic spins. Our approach combines analytic insights from post-Newtonian theory with data-driven techniques to ensure correct limiting behavior and high accuracy across parameter space. Both gwModel_kick_q200 and gwModel_kick_prec_flow are valid from comparable to extreme mass ratios. Extensive validation shows all three models outperform existing ones within their respective domains. Finally, using both back-of-the-envelope estimates and 1404 detailed star cluster simulations incorporating our kick models, we find that the black hole retention probability in low mass globular clusters can vary noticeably when the gwModel_kick_prec_flow model is employed. The models are publicly available through the gwModels package.