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Hierarchical Inference of Binary Neutron Star Mass Distribution and Equation of State with Gravitational Waves

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arxiv 2106.15745 v2 pith:D4XZ4WOG submitted 2021-06-29 astro-ph.HE gr-qc

Jacob Golomb , Colm Talbot This is my paper · ORCID
classification astro-ph.HEgr-qc
keywords equationneutronstatestardistributioninferencebinarymass
verification ladder T0 review T1 audit T2 compute T3 formal
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Gravitational wave observations of binary neutron star mergers provide valuable information about neutron star structure and the equation of state of dense nuclear matter. Numerous methods have been proposed to analyze the population of observed neutron stars and previous work has demonstrated the necessity of jointly fitting the astrophysical distribution and the equation of state in order to accurately constrain the equation of state. In this work, we introduce a new framework to simultaneously infer the distribution of binary neutron star masses and the nuclear equation of state using Gaussian mixture model density estimates which mitigates some of the limitations previously-used methods suffer from. Using our method, we reproduce previous projections for the expected precision of our joint mass distribution and equation of state inference with tens of observations. We also show that mismodeling the equation of state can bias our inference of the neutron star mass distribution. While we focus on neutron star masses and matter effects, our method is widely applicable to population inference problems.

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Cited by 3 Pith papers

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

  1. Binary neutron stars in the next-generation era: Multi-messenger detection prospects and constraints on the equation of state, mass distribution, and cosmology

    astro-ph.HE 2026-07 conditional novelty 6.0 of 10

    With ET (and ET+CE), mock multi-messenger BNS catalogues yield ~40–500 EM counterparts per year and, under ideal recovery, constrain R1.4 to ~0.2 km and H0 to ~1 km s−1 Mpc−1.

  2. Fortifying gravitational-wave population inference with normalizing flows

    astro-ph.HE 2026-06 conditional novelty 6.0 of 10

    Representing each gravitational-wave event's posterior with a normalizing flow lets analysts generate enough cheap posterior samples to keep the Monte-Carlo variance of population inference below threshold for catalog...

  3. Cosmic Calipers: Precise and Accurate Neutron Star Radius Measurements with Next-Generation Gravitational Wave Detectors

    astro-ph.HE 2025-02 conditional novelty 5.0 of 10

    Future gravitational wave detectors should measure most neutron star radii to within about 5%, while current detectors give biased and imprecise estimates.

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