Accelerated Bayesian parameter estimation and model selection for gravitational waves with normalizing flows
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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× and 15× 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.