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arXiv · 2410.21076

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

Alicja Polanska·Thibeau Wouters·Peter T. H. Pang·Kaze K. W. Wong·Jason D. McEwen
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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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Alicja Polanska, Thibeau Wouters, Peter T. H. Pang, Kaze K. W. Wong, Jason D. McEwen. 2024-10-28. Accelerated Bayesian parameter estimation and model selection for gravitational waves with normalizing flows. https://arxiv.org/abs/2410.21076

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