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

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.

16 pages, 5 figures

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• Quasinormal modes of dS and AdS Black Holes: Feedforward neural network method
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• Applications of machine learning in gravitational wave research with current interferometric detectors
• Merger-Ringdown Consistency: A New Test of Strong Gravity using Deep Learning
• Extending black-hole remnant surrogate models to extreme mass ratios
• Optimizing the Placement of Numerical Relativity Simulations using a Mismatch Predicting Neural Network
• Building a bridge between comparable and extreme mass ratio black hole binaries: a single spin precessing model for the final state