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Uncovering faint lensed gravitational-wave signals and reprioritizing their follow-up analysis using galaxy lensing forecasts with detected counterparts

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arxiv 2403.16532 v3 pith:VWSSNH7J submitted 2024-03-25 gr-qc

classification gr-qc
keywords lensedimageslensinggalaxysignalsarrivalcandidatefollow-up
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Like light, gravitational waves can be gravitationally lensed by massive astrophysical objects. Strong gravitational lensing by galaxies and galaxy clusters is anticipated to become observable in the coming years. This phenomenon will manifest as multiple copies of the original wave, each exhibiting identical frequency evolution but distinct arrival times, amplitudes, and overall phases. Some of these images can be below the detection threshold and require targeted search methods, based on tailor-made template banks. These searches can be made more sensitive by using our knowledge of the typical distribution and morphology of lenses to predict the time delay, magnification, and image-type ordering of the lensed images. Here, we show that when a subset of the galaxy lensed images is super-threshold, they can be used to construct a more constrained prediction of the arrival time of the remaining signals, enhancing our ability to identify lensing candidate signals. Our suggested method effectively reduces the list of triggers requiring follow-up and generally re-ranks the genuine counterpart higher in the lensing candidate list. So, using information provided by the two or three super-threshold images, one can identify additional lensed images, also strengthening the evidence for the lensed signal hypothesis.

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Cited by 2 Pith papers

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

  1. Fast and efficient Bayesian method to search for strongly lensed gravitational waves

    gr-qc 2024-12 conditional novelty 6.0 of 10

    PO2.0 reweights single-event posteriors to compute a lensing Bayes factor that includes population priors and lensing-biased parameters, detecting 65% of simulated galaxy-lensed BBH pairs at a pairwise false-alarm pro...

  2. gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves

    astro-ph.IM 2024-12 conditional novelty 4.0 of 10

    gwsnr is a Python package that computes gravitational-wave signal-to-noise ratios and detection probabilities for large compact-binary populations using interpolation, neural networks, and GPU acceleration.

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