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

11/04/2019

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by   Leïla Haegel, et al.

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We present the first estimation of the mass and spin of Kerr black holes resulting from the coalescence of binary black holes using a deep neural network. The network is trained on the full publicly available catalog of numerical simulations of gravitational waves emission by binary black hole systems. The network prediction for non-precessing binaries as well as precessing binaries is compared with existing fits in the LIGO-Virgo software package when existing. For the non-precessing case, the absolute error distribution has a root mean square error of 2.6 · 10^-3 for the final mass (twice lower than the existing fits) and 3 · 10^-3 for the final spin (similarly to the existing fits). We also estimate of the final mass in the precessing case, where we obtain a RMSE of 1 · 10^-3 of the absolute error distribution. It is 8 · 10^-3 when predicting the spin of the black hole resulting from a precessing binary, against 1.1 · 10^-2 for the existing fits.