Accelerated Bayesian parameter estimation and model selection for gravitational waves with normalizing flows Article Swipe
Alicja Polanska
,
Thibeau Wouters
,
P. T. H. Pang
,
Kaze W. K. Wong
,
Jason D. McEwen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.21076
YOU?
·
· 2024
· Open Access
·
· 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.
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- 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
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https://openalex.org/W4404346302Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2410.21076Digital Object Identifier
- Title
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Accelerated Bayesian parameter estimation and model selection for gravitational waves with normalizing flowsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-10-28Full publication date if available
- Authors
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Alicja Polanska, Thibeau Wouters, P. T. H. Pang, Kaze W. K. Wong, Jason D. McEwenList of authors in order
- Landing page
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https://arxiv.org/abs/2410.21076Publisher landing page
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https://arxiv.org/pdf/2410.21076Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2410.21076Direct OA link when available
- Concepts
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Bayesian probability, Gravitational wave, Model selection, Selection (genetic algorithm), Estimation, Econometrics, Bayesian inference, Estimation theory, Mathematics, Computer science, Statistical physics, Applied mathematics, Statistics, Physics, Economics, Artificial intelligence, Astrophysics, ManagementTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.techniques | 9, 64 |
| abstract_inverted_index.thoroughly | 97 |
| abstract_inverted_index.accelerated | 3 |
| abstract_inverted_index.computation | 73 |
| abstract_inverted_index.demonstrate | 22 |
| abstract_inverted_index.normalizing | 11, 36 |
| abstract_inverted_index.open-source | 99 |
| abstract_inverted_index.traditional | 61 |
| abstract_inverted_index.well-tested | 95 |
| abstract_inverted_index.accessibility | 102 |
| abstract_inverted_index.astrophysics. | 28 |
| abstract_inverted_index.flow-enhanced | 37 |
| abstract_inverted_index.gravitational | 26, 85 |
| abstract_inverted_index.respectively. | 89 |
| abstract_inverted_index.$4$-dimensional | 82 |
| abstract_inverted_index.reproducibility | 104 |
| abstract_inverted_index.$11$-dimensional | 84 |
| abstract_inverted_index.high-performance | 7 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |