Detection and parameter estimation of gravitational waves from compact binary mergers using Deep Learning

One of the key challenges of real-time detection and parameter estimation of gravitational waves (GWs) from compact binary mergers is the computational cost of conventional matched-filtering and Bayesian inference approaches. In particular, the application of these methods to the full signal parameter space available to the gravitational-wave detectors, and/or real-time parameter estimation is computationally prohibitive. On the other hand, rapid detection and inference are critical for prompt follow-up of the electromagnetic and astro-particle counterparts accompanying important transients, such as binary neutron-star (BNS) and black-hole neutron-star (BHNS) mergers. Training deep neural networks to identify specific signals and learn a computationally efficient representation of the mapping between GW signals and their parameters allows both detection and inference to be done quickly and reliably, with high sensitivity and accuracy. We present our results of detection and characterization of GWs from compact binary mergers in real LIGO data, with a particular attention to systems involving neutron stars. The implications for detection and interpretation of recent and future GW signals from BNS and BHNS mergers, and the equation of state (EOS) of dense matter will be discussed.