A Machine Learning-based Source Property Inference for Compact Binary Mergers

The detection of the binary neutron star merger, GW170817, was the first success story of multi-messenger observations of compact binary mergers. The inferred merger rate, along with the increased sensitivity of the ground-based gravitational-wave (GW) network in the present LIGO/Virgo, and future LIGO/Virgo/KAGRA observing runs, strongly hints at detections of binaries that could potentially have an electromagnetic (EM) counterpart. A rapid assessment of properties that could lead to a counterpart is essential to aid time-sensitive follow-up operations, especially robotic telescopes. At minimum, the possibility of counterparts requires a neutron star (NS). Also, the tidal disruption physics is important to determine the remnant matter post-merger, the dynamics of which could result in the counterparts. The main challenge, however, is that the binary system parameters, such as masses and spins estimated from the real-time, GW template-based searches, are often dominated by statistical and systematic errors. Here, we present an approach that uses supervised machine learning to mitigate such selection effects to report the possibility of counterparts based on the presence of an NS component, and the presence of remnant matter post-merger in real time.