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Rapid Identification of Strongly Lensed Gravitational-Wave Events with Machine Learning
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Rapid Identification of Strongly Lensed Gravitational-Wave Events with Machine Learning
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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.
Forward citations
Cited by 4 Pith papers
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Bayesian analysis of simulated Taiji observations shows microlensing from lenses above 10^5 solar masses can be distinguished from unlensed DWD signals when separation is below 3 Einstein radii, while lower masses or ...
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