Black-hole kicks from numerical-relativity surrogate models

TL;DR

This paper introduces a fast, reliable method using surrogate models to compute black-hole recoil velocities from gravitational wave data, enabling large-scale astrophysical studies.

Contribution

We develop a surrogate-based approach to efficiently extract black-hole recoil velocities directly from gravitational waveforms, bypassing traditional fitting formulae.

Findings

The method accurately reproduces recoil velocities across parameter space.

Evaluation time per case is approximately 0.1 seconds.

The approach is publicly available as the SURRKICK Python module.

Abstract

Binary black holes radiate linear momentum in gravitational waves as they merge. Recoils imparted to the black-hole remnant can reach thousands of km/s, thus ejecting black holes from their host galaxies. We exploit recent advances in gravitational waveform modeling to quickly and reliably extract recoils imparted to generic, precessing, black hole binaries. Our procedure uses a numerical-relativity surrogate model to obtain the gravitational waveform given a set of binary parameters, then from this waveform we directly integrate the gravitational-wave linear momentum flux. This entirely bypasses the need of fitting formulae which are typically used to model black-hole recoils in astrophysical contexts. We provide a thorough exploration of the black-hole kick phenomenology in the parameter space, summarizing and extending previous numerical results on the topic. Our extraction procedure is made publicly available as a module for the Python programming language named SURRKICK. Kick evaluations take ~0.1s on a standard off-the-shelf machine, thus making our code ideal to be ported to large-scale astrophysical studies.