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

Understand

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.