High-accuracy mass, spin, and recoil predictions of generic black-hole merger remnants

TL;DR

This paper introduces highly accurate models for predicting the properties of black-hole merger remnants, including mass, spin, and recoil velocity, using Gaussian process regression trained on extensive numerical relativity data.

Contribution

The authors develop novel, highly precise fitting formulas for remnant properties of precessing and aligned-spin black-hole binaries, surpassing existing models in accuracy and applicability.

Findings

Errors in remnant mass and spin predictions are reduced by at least an order of magnitude.

Models accurately predict remnant properties across a broad parameter space.

The models are publicly available as a Python module for gravitational-wave data analysis.

Abstract

We present accurate fits for the remnant properties of generically precessing binary black holes, trained on large banks of numerical-relativity simulations. We use Gaussian process regression to interpolate the remnant mass, spin, and recoil velocity in the 7-dimensional parameter space of precessing black-hole binaries with mass ratios $q\leq2$, and spin magnitudes $\chi_1,\chi_2\leq0.8$. For precessing systems, our errors in estimating the remnant mass, spin magnitude, and kick magnitude are lower than those of existing fitting formulae by at least an order of magnitude (improvement is also reported in the extrapolated region at high mass ratios and spins). In addition, we also model the remnant spin and kick directions. Being trained directly on precessing simulations, our fits are free from ambiguities regarding the initial frequency at which precessing quantities are defined. We also construct a model for remnant properties of aligned-spin systems with mass ratios $q\leq8$, and spin magnitudes $\chi_1,\chi_2\leq0.8$. As a byproduct, we also provide error estimates for all fitted quantities, which can be consistently incorporated into current and future gravitational-wave parameter-estimation analyses. Our model(s) are made publicly available through a fast and easy-to-use Python module called surfinBH.