Ranking Candidate Signals with Machine Learning in Low-Latency Search for Gravitational-Waves from Compact Binary Mergers

Understand

In the multi-messenger astronomy era, accurate sky localization and low latency time of gravitational-wave (GW) searches are keys in triggering successful follow-up observations on the electromagnetic counterpart of GW signals.

• We, in this work, focus on the latency time and study the feasibility of adopting supervised machine learning (ML) method for ranking candidate GW events.
• We consider two popular ML methods, random forest and neural networks.
• We observe that the evaluation time of both methods takes tens of milliseconds for $\sim$ 45,000 evaluation samples.