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Probing the nature of dark matter using strongly lensed gravitational waves from binary black holes

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arxiv 2408.05290 v1 pith:YVBBCJ6L submitted 2024-08-09 astro-ph.CO

classification astro-ph.CO
keywords lenseddarkmattereventsdistributionexpectedmassnumber
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abstract

Next-generation ground-based gravitational-wave (GW) detectors are expected to detect millions of binary black hole mergers during their operation period. A small fraction ($\sim 0.1 - 1\%$) of them will be strongly lensed by intervening galaxies and clusters, producing multiple copies of the GW signals. The expected number of lensed events and the distribution of the time delay between lensed images will depend on the mass distribution of the lenses at different redshifts. Warm dark matter or fuzzy dark matter models predict lower abundances of small mass dark matter halos as compared to the standard cold dark matter. This will result in a reduction in the number of strongly lensed GW events, especially at small time delays. Using the number of lensed events and the lensing time delay distribution, we can put a lower bound on the mass of the warm/fuzzy dark matter particle from a catalog of lensed GW events. The expected bounds from GW strong lensing from next-generation detectors are significantly better than the current constraints.

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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. 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.

  2. 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.

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