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

TL;DR

This paper investigates using machine learning, specifically random forests and neural networks, to improve the ranking and detection efficiency of gravitational-wave signals from compact binary mergers with low latency.

Contribution

It demonstrates that ML methods can significantly enhance search sensitivity and reduce false positives in low-latency gravitational-wave detection compared to traditional methods.

Findings

ML evaluation time is tens of milliseconds for 45,000 samples

ML methods improve detection efficiency by about 10% at low false positive rates

Search sensitivity can be increased by approximately 18% at very low false alarm rates

Abstract

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. 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 $\sim 2 \times 10^{-5}$ with both ML methods. We also present that the search sensitivity can be enhanced by about 18\% at $\sim 10^{-11}$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.