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Ain't No Mountain High Enough: Semi-Parametric Modeling of LIGO-Virgos Binary Black Hole Mass Distribution

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arxiv 2109.06137 v2 pith:ZPNOVTJJ submitted 2021-09-13 astro-ph.HE

classification astro-ph.HE
keywords modeldistributionmasspeakperturbationprimarysemi-parametrictextsc
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abstract

We introduce a semi-parametric model for the primary mass distribution of binary black holes (BBHs) observed with gravitational waves (GWs) that applies a cubic-spline perturbation to a power law. We apply this model to the 46 BBHs included in the second gravitational wave transient catalog (GWTC-2). The spline perturbation model recovers a consistent primary mass distribution with previous results, corroborating the existence of a peak at $35\,M_\odot$ ($>97\%$ credibility) found with the \textsc{Powerlaw+Peak} model. The peak could be the result pulsational pair-instability supernovae (PPISNe). The spline perturbation model finds potential signs of additional features in the primary mass distribution at lower masses similar to those previously reported by Tiwari and Fairhurst (2021). However, with fluctuations due to small number statistics, the simpler \textsc{Powerlaw+Peak} and \textsc{BrokenPowerlaw} models are both still perfectly consistent with observations. Our semi-parametric approach serves as a way to bridge the gap between parametric and non-parametric models to more accurately measure the BBH mass distribution. With larger catalogs we will be able to use this model to resolve possible additional features that could be used to perform cosmological measurements, and will build on our understanding of BBH formation, stellar evolution and nuclear astrophysics.

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

    astro-ph.HE 2026-07 conditional novelty 7.0 of 10

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  2. When (not) to trust Monte Carlo approximations for hierarchical Bayesian inference

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    A unified error statistic E-hat measures information lost to Monte Carlo noise in hierarchical Bayesian inference, with a recommended cutoff of 0.2 bits.

  3. A new group of low-spin $50-70M_\odot$ Black Holes and the high pair-instability mass cutoff

    astro-ph.HE 2025-10 conditional novelty 6.0 of 10

    GWTC-4.0 reveals low-spin black holes up to ~68.5 Msun, implying a higher pair-instability mass cutoff and a lower 12C(alpha,gamma)16O rate.

  4. Aligned Hierarchical Black Hole Mergers in Active-Galactic-Nuclei Disks Revealed by GWTC-4

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

    GWTC-4 reveals a high-spin, high-mass black hole subpopulation with spin-orbit alignment, interpreted as evidence for hierarchical mergers in AGN disks.

  5. The Long Road to Alignment: Measuring Black Hole Spin Orientation with Expanding Gravitational-Wave Datasets

    astro-ph.HE 2025-05 conditional novelty 6.0 of 10

    Simulated gravitational-wave catalogs show spin-tilt peaks at alignment are hard to confirm even with 1500 events, while integrated tilt fractions are robust.

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