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Beyond the detector horizon: Forecasting gravitational-wave strong lensing

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arxiv 2106.06303 v3 pith:SO4XEVMN submitted 2021-06-11 astro-ph.HE gr-qc

classification astro-ph.HEgr-qc
keywords lensingsearchesstrongforecaststimealarmassociateddelays
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abstract

When gravitational waves pass near massive astrophysical objects, they can be gravitationally lensed. The lensing can split them into multiple wave-fronts, magnify them, or imprint beating patterns on the waves. Here we focus on the multiple images produced by strong lensing. In particular, we investigate strong lensing forecasts, the rate of lensing, and the role of lensing statistics in strong lensing searches. Overall, we find a reasonable rate of lensed detections for double, triple, and quadruple images at the LIGO--Virgo--KAGRA design sensitivity. We also report the rates for A+ and LIGO Voyager and briefly comment on potential improvements due to the inclusion of sub-threshold triggers. We find that most galaxy-lensed events originate from redshifts $z \sim 1-4$ and report the expected distribution of lensing parameters for the observed events. Besides forecasts, we investigate the role of lensing forecasts in strong lensing searches, which explore repeated event pairs. One problem associated with the searches is the rising number of event pairs, which leads to a rapidly increasing false alarm probability. We show how knowledge of the expected galaxy lensing time delays in our searches allow us to tackle this problem. Once the time delays are included, the false alarm probability increases linearly (similar to non-lensed searches) instead of quadratically with time, significantly improving the search. For galaxy cluster lenses, the improvement is less significant. The main uncertainty associated with these forecasts are the merger-rate density estimates at high redshift, which may be better resolved in the future.

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Forward citations

Cited by 6 Pith papers

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

  1. Across the Universe: GW231123 as a magnified and diffracted black hole merger

    astro-ph.GA 2025-12 conditional novelty 6.0 of 10

    GW231123's extreme mass and spins may be explained by a point-mass microlens embedded in a galaxy, reducing the inferred source mass to about 100-180 solar masses.

  2. 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.

  3. Prospect for Detection of Strongly Lensed Multi-messenger Signals of Binary Neutron Star Mergers

    astro-ph.HE 2026-07 conditional novelty 5.0 of 10

    Future CE+ET detectors may detect lensed BNS kilonovae at ~0.5/yr via pointed follow-up of known galaxy lenses, while lensed sGRBs and afterglows remain rare or undetectable with current-generation facilities.

  4. Discovering gravitational waveform distortions from lensing: A deep dive into GW231123

    gr-qc 2025-12 conditional novelty 5.0 of 10

    GW231123's apparent gravitational-lensing signal has a false-alarm probability around 4σ, so the event cannot be claimed as lensed under the two-image wave-optics model.

  5. Self-lensing of moving gravitational-wave sources can break the microlensing crossing timescale degeneracy

    astro-ph.HE 2025-12 conditional novelty 5.0 of 10

    Self-lensing of a moving GW chirp by an orbiting black hole yields a curve width and interference beats that together give the orbital distance and the black hole mass.

  6. Accelerated inference of microlensed gravitational waves with machine learning

    astro-ph.CO 2025-11 conditional novelty 5.0 of 10

    A neural posterior estimator trained on wave-optics-microlensed gravitational-wave signals recovers source and lens parameters and Bayes factors consistent with Bilby, about 10 times faster.

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