Ranking candidate signals with machine learning in low-latency searches for gravitational waves from compact binary mergers

In the multimessenger 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, study the feasibility of adopting a supervised machine learning (ML) method for scoring rank on 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 ∼45,000 evaluation samples. We compare the classification efficiency between the two ML methods and a conventional low-latency search method with respect to the true positive rate at given false positive rate. The comparison shows that about 10% improved efficiency can be achieved at lower false positive rate ∼2×10⁻⁵ with both ML methods. We also present that the search sensitivity can be enhanced by about 18% at ∼10⁻¹¹ Hz false alarm rate. We conclude that adopting ML methods for ranking candidate GW events is a prospective approach to yield low latency and high efficiency in searches for GW signals from compact binary mergers