High-Dimensional Bayesian Model Comparison in Cosmology with GPU-accelerated Nested Sampling and Neural Emulators
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909828282056704 |
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| author | Lovick, Toby Yallup, David Piras, Davide Mancini, Alessio Spurio Handley, Will |
| author_facet | Lovick, Toby Yallup, David Piras, Davide Mancini, Alessio Spurio Handley, Will |
| contents | We demonstrate a GPU-accelerated nested sampling framework for efficient high-dimensional Bayesian inference in cosmology. Using JAX-based neural emulators and likelihoods for cosmic microwave background and cosmic shear analyses, our approach provides parameter constraints and direct calculation of Bayesian evidence. In the 39-dimensional $Λ$CDM vs $w_0w_a$ shear analysis, we produce Bayes factors and a robust error bar in just 2 days on a single A100 GPU, without loss of accuracy. Where CPU-based nested sampling can now be outpaced by methods relying on MCMC sampling and decoupled evidence estimation, we demonstrate that with GPU acceleration nested sampling offers the necessary speed-up to put it on equal computational footing with these methods, especially where reliable model comparison is paramount. We also explore interpolation in the matter power spectrum for cosmic shear analysis, finding a further factor of 4 speed-up with consistent posterior contours and Bayes factor. We put forward both nested and gradient-based sampling as useful tools for the modern cosmologist, where cutting-edge inference pipelines can yield orders of magnitude improvements in computation time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13307 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | High-Dimensional Bayesian Model Comparison in Cosmology with GPU-accelerated Nested Sampling and Neural Emulators Lovick, Toby Yallup, David Piras, Davide Mancini, Alessio Spurio Handley, Will Cosmology and Nongalactic Astrophysics We demonstrate a GPU-accelerated nested sampling framework for efficient high-dimensional Bayesian inference in cosmology. Using JAX-based neural emulators and likelihoods for cosmic microwave background and cosmic shear analyses, our approach provides parameter constraints and direct calculation of Bayesian evidence. In the 39-dimensional $Λ$CDM vs $w_0w_a$ shear analysis, we produce Bayes factors and a robust error bar in just 2 days on a single A100 GPU, without loss of accuracy. Where CPU-based nested sampling can now be outpaced by methods relying on MCMC sampling and decoupled evidence estimation, we demonstrate that with GPU acceleration nested sampling offers the necessary speed-up to put it on equal computational footing with these methods, especially where reliable model comparison is paramount. We also explore interpolation in the matter power spectrum for cosmic shear analysis, finding a further factor of 4 speed-up with consistent posterior contours and Bayes factor. We put forward both nested and gradient-based sampling as useful tools for the modern cosmologist, where cutting-edge inference pipelines can yield orders of magnitude improvements in computation time. |
| title | High-Dimensional Bayesian Model Comparison in Cosmology with GPU-accelerated Nested Sampling and Neural Emulators |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2509.13307 |