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

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arxiv 2106.12466 v1 pith:XTH5BJLF submitted 2021-06-23 gr-qc astro-ph.HEastro-ph.IM

Srashti Goyal , Harikrishnan D. , Shasvath J. Kapadia , Parameswaran Ajith This is my paper · ORCID
classification gr-qcastro-ph.HEastro-ph.IM
keywords lensedmachineeventspairsbayesiandetecteddetectorsgeneration
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Probability of gravitational-wave lensing by intermediate-mass black holes and globular clusters

    astro-ph.CO 2026-08 conditional novelty 6.0 of 10

    The rate of compound gravitational-wave lensing by intermediate-mass black holes in globular clusters is at most about 10^-3 of galaxy-scale lensed events, disfavoring GW231123 as such an event.

  2. Identifying lensed gravitational waves with physics-informed posterior learning

    gr-qc 2026-07 conditional novelty 6.0 of 10

    Fusing a simulation-trained common-source mass posterior with waveform features raises lensed-event detection efficiency from 20.8% to 35.2% at 1% false-positive rate and lowers the SNR for 50% efficiency from 45.3 to 33.5.

  3. Identifying Microlensing by Compact Dark Matter through Diffraction Patterns in Gravitational Waves with Machine Learning

    astro-ph.IM 2025-09 conditional novelty 6.0 of 10

    A wavelet-convolution neural network distinguishes simulated lensed from unlensed gravitational waves with 92.2% accuracy (AUC 0.965) using wave-optics diffraction patterns.

  4. A $\chi^2$ statistic for the identification of strongly lensed gravitational waves from compact binary coalescences

    gr-qc 2025-02 conditional novelty 6.0 of 10

    A chi-squared test built from gravitational wave search templates can separate lensed event pairs from unlensed pairs with accuracy comparable to slower Bayesian methods.

  5. Machine Learning Assisted Parameter-Space Searches for Lensed Gravitational Waves

    gr-qc 2025-09 conditional novelty 5.0 of 10

    Normalizing flow based non-Gaussian consistency tests in a compressed detector basis select GW170104-GW170814 as the only promising lensed pair in GWTC-3.

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