Rapid Mass Parameter Estimation of Binary Black Hole Coalescences Using Deep Learning

TL;DR

This paper demonstrates that deep learning models can rapidly estimate key parameters of binary black hole mergers from gravitational wave data, achieving accuracy comparable to traditional methods but with significantly reduced processing time.

Contribution

The authors introduce neural network models for fast posterior estimation of binary black hole parameters, matching LALInference accuracy while greatly decreasing computation time.

Findings

Neural networks predict posterior distributions consistent with LALInference.

Models achieve median predictions within 90% credible intervals for real events.

Deep learning enables low-latency, high-accuracy parameter estimation for real-time GW analysis.

Abstract

Deep learning can be used to drastically decrease the processing time of parameter estimation for coalescing binaries of compact objects including black holes and neutron stars detected in gravitational waves (GWs). As a first step, we present two neural network models trained to rapidly estimate the posterior distributions of the chirp mass and mass ratio of a detected binary black hole system from the GW strain data of LIGO Hanford and Livingston Observatories. Using these parameters the component masses can be predicted, which has implications for the prediction of the likelihood that a merger contains a neutron star. The results are compared to the 'gold standard' of parameter estimation of gravitational waves used by the LIGO-Virgo Collaboration (LVC), LALInference. Our models predict posterior distributions consistent with that from LALInference while using orders of magnitude less processing time once the models are trained. The median predictions are within the 90% credible intervals of LALInference for all predicted parameters when tested on real binary black hole events detected during the LVC's first and second observing runs. We argue that deep learning has strong potential for low-latency high-accuracy parameter estimation suitable for real-time GW search pipelines.