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Accelerated Bayesian parameter estimation and model selection for gravitational waves with normalizing flows

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arxiv 2410.21076 v2 pith:RPZDVKPM submitted 2024-10-28 astro-ph.IM astro-ph.HEcs.LGgr-qc

Alicja Polanska , Thibeau Wouters , Peter T. H. Pang , Kaze K. W. Wong , Jason D. McEwen This is my paper · ORCID
classification astro-ph.IMastro-ph.HEcs.LGgr-qc
keywords bayesiangravitationalnormalizingaccelerateddimensionalestimationflowsinference
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

We present an accelerated pipeline, based on high-performance computing techniques and normalizing flows, for joint Bayesian parameter estimation and model selection and demonstrate its efficiency in gravitational wave astrophysics. We integrate the Jim inference toolkit, a normalizing flow-enhanced Markov chain Monte Carlo (MCMC) sampler, with the learned harmonic mean estimator. Our Bayesian evidence estimates run on $1$ GPU are consistent with traditional nested sampling techniques run on $16$ CPU cores, while reducing the computation time by factors of $5\times$ and $15\times$ for $4$-dimensional and $11$-dimensional gravitational wave inference problems, respectively. Our code is available in well-tested and thoroughly documented open-source packages, ensuring accessibility and reproducibility for the wider research community.

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Cited by 3 Pith papers

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  1. Unified remnant models for aligned-spin, precessing, and eccentric binary black hole mergers

    gr-qc 2026-08 conditional novelty 6.0 of 10

    New analytic fits, gwModelRemS/P, predict remnant mass, spin, luminosity, and kick for black hole mergers from equal mass to q=1000, with a neural-flow model for precessing kicks.

  2. Model-agnostic search of gravitational wave echoes in LVK data

    gr-qc 2025-12 conditional novelty 6.0 of 10

    A multi-detector phase-marginalized echo search finds no statistically significant long-lived quasinormal-mode echoes in GW150914, GW231226, and GW250114, and sets 90% upper limits on their network SNR and amplitude.

  3. Savage-Dickey density ratio estimation with normalizing flows for Bayesian model comparison

    astro-ph.CO 2025-06 conditional novelty 5.0 of 10

    A normalizing flow estimates the normalized marginal posterior in the Savage-Dickey density ratio, enabling Bayes factors for nested models with many extra parameters.

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