Rapid Identification of Strongly Lensed Gravitational-Wave Events with Machine Learning

TL;DR

This paper introduces a machine learning approach to quickly identify strongly lensed gravitational-wave events, significantly reducing computational effort compared to traditional Bayesian methods.

Contribution

The authors develop and demonstrate a machine learning method that rapidly screens candidate lensed GW pairs using time-frequency and localization data, matching Bayesian accuracy.

Findings

Machine learning accurately identifies lensed pairs.

Method reduces computational time significantly.

Comparable performance to Bayesian techniques.

Abstract

A small fraction of the gravitational-wave (GW) signals that will be detected by second and third generation detectors are expected to be strongly lensed by galaxies and clusters, producing multiple observable copies. While optimal Bayesian model selection methods are developed to identify lensed signals, processing tens of thousands (billions) of possible pairs of events detected with second (third) generation detectors is both computationally intensive and time consuming. To mitigate this problem, we propose to use machine learning to rapidly rule out a vast majority of candidate lensed pairs. As a proof of principle, we simulate non-spinning binary black hole events added to Gaussian noise, and train the machine on their time-frequency maps (Q-transforms) and localisation skymaps (using Bayestar), both of which can be generated in seconds. We show that the trained machine is able to accurately identify lensed pairs with efficiencies comparable to existing Bayesian methods.