Predicting the properties of black holes merger remnants with Deep Neural Networks

TL;DR

This paper introduces a deep neural network that accurately predicts the mass and spin of black hole merger remnants, outperforming existing methods and reducing errors significantly.

Contribution

The paper presents the first neural network model trained on numerical simulations to estimate black hole merger remnant properties with high accuracy.

Findings

Predicts remnant mass and spin with errors less than 0.04% and 0.3% for most cases.

Reduces root mean square error of remnant mass by half compared to existing fits.

Corrects biases in current models for precessing binary black hole mergers.

Abstract

We present the first estimation of the mass and spin magnitude of Kerr black holes resulting from the coalescence of binary black holes using a deep neural network. The network is trained on a dataset containing 80\% of the full publicly available catalog of numerical simulations of gravitational waves emission by binary black hole systems, including full precession effects for spinning binaries. The network predicts the remnant black holes mass and spin with an error less than 0.04\% and 0.3\% respectively for 90\% of the values in the non-precessing test dataset, it is 0.1\% and 0.3\% respectively in the precessing test dataset. When compared to existing fits in the LIGO algorithm software library, the network enables to reduce the remnant mass root mean square error to one half in the non-precessing case. In the precessing case, both remnant mass and spin mean square errors are decreased to one half, and the network corrects the bias observed in available fits.