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

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.

11 pages, 6 figures

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• Multi-messenger Gravitational Lensing
• Deep Learning-based Search for Microlensing Signature from Binary Black Hole Events in GWTC-1 and -2
• SLICK: Strong Lensing Identification of Candidates Kindred in gravitational wave data
• GLANCE -- Gravitational Lensing Authenticator using Non-Modelled Cross-Correlation Exploration of Gravitational Wave Signals
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• Waveform systematics in identifying strongly gravitationally lensed gravitational waves: Posterior overlap method
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• Gravitational Lensing of Gravitational Waves from Astrophysical Sources: Theory, Detection, and Applications
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