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Characterizing the observation bias in gravitational-wave detections and finding structured population properties

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arxiv 2105.13983 v2 pith:W2BQA2GB submitted 2021-05-28 gr-qc astro-ph.HEastro-ph.IM

Characterizing the observation bias in gravitational-wave detections and finding structured population properties

classification gr-qc astro-ph.HEastro-ph.IM
keywords biasdetectionsbinarygravitational-waveinferencesmethodobservationodot
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The observed distributions of the source properties from gravitational-wave detections are biased due to the selection effects and detection criteria in the detections, analogous to the Malmquist bias. In this work, this observation bias is investigated through its fundamental statistical and physical origins. An efficient semi-analytical formulation for its estimation is derived which is as accurate as the standard method of numerical simulations, with only a millionth of the computational cost. Then, the estimated bias is used for unmodelled inferences on the binary black hole population. These inferences show additional structures, specifically two peaks in the joint mass distribution around binary masses $\sim10$ M$_\odot$ and $\sim30$ M$_\odot$. Example ready-to-use scripts and some produced datasets for this method are shared in an online repository.

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Cited by 2 Pith papers

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

  1. A Four-dimensional Model-agnostic Probe into the Astrophysical Origins of Binary Black Hole Subpopulations

    astro-ph.HE 2026-07 conditional novelty 7.0

    A GPU-accelerated binned Gaussian process yields the first model-agnostic 4D BBH population in (m1, q, χeff, χp), revealing four mass-based subpopulations and new spin-mass-ratio correlations.

  2. An Implementation to Identify the Properties of Multiple Population of Gravitational Wave Sources

    gr-qc 2025-09 unverdicted novelty 4.0

    GWKokab is a new modular JAX framework that uses normalizing flow samplers for efficient inference on subpopulations of compact binary mergers.