Extending black-hole remnant surrogate models to extreme mass ratios

TL;DR

This paper introduces a machine-learning method that extends black-hole remnant models to extreme mass ratios, enabling accurate predictions across a broader parameter space in gravitational-wave astronomy.

Contribution

The authors develop a Gaussian process regression model that combines numerical-relativity and analytical data to accurately predict black-hole remnants at extreme mass ratios.

Findings

Model achieves accuracy comparable or superior to existing models.

Validates performance through cross-validation and additional simulations.

Provides robust predictions for arbitrary mass ratios.

Abstract

Numerical-relativity surrogate models for both black-hole merger waveforms and remnants have emerged as important tools in gravitational-wave astronomy. While producing very accurate predictions, their applicability is limited to the region of the parameter space where numerical-relativity simulations are available and computationally feasible. Notably, this excludes extreme mass ratios. We present a machine-learning approach to extend the validity of existing and future numerical-relativity surrogate models toward the test-particle limit, targeting in particular the mass and spin of post-merger black-hole remnants. Our model is trained on both numerical-relativity simulations at comparable masses and analytical predictions at extreme mass ratios. We extend the gaussian-process-regression model NRSur7dq4Remnant, validate its performance via cross validation, and test its accuracy against additional numerical-relativity runs. Our fit, which we dub NRSur7dq4EmriRemnant, reaches an accuracy that is comparable to or higher than that of existing remnant models while providing robust predictions for arbitrary mass ratios.