Accelerated Bayesian parameter estimation and model selection for gravitational waves with normalizing flows Article Swipe
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· 2024
· Open Access
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· DOI: https://doi.org/10.48550/arxiv.2410.21076
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.
Related Topics To Compare & Contrast
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2410.21076
- https://arxiv.org/pdf/2410.21076
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404346302