Uncertainty-aware and Data-efficient Cosmological Emulation using Gaussian Processes and PCA
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866914828537298944 |
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| author | Günther, Sven |
| author_facet | Günther, Sven |
| contents | Bayesian parameter inference is one of the key elements for model selection in cosmological research. However, the available inference tools require a large number of calls to simulation codes which can lead to high and sometimes even infeasible computational costs. In this work we propose a new way of emulating simulation codes for Bayesian parameter inference. In particular, this novel approach emphasizes the uncertainty-awareness of the emulator, which allows to state the emulation accuracy and ensures reliable performance. With a focus on data efficiency, we implement an active learning algorithm based on a combination of Gaussian Processes and Principal Component Analysis. We find that for an MCMC analysis of Planck and BAO data on the $Λ$CDM model (6 model and 21 nuisance parameters) we can reduce the number of simulation calls by a factor of $\sim$500 and save about $96\%$ of the computational costs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_01138 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Uncertainty-aware and Data-efficient Cosmological Emulation using Gaussian Processes and PCA Günther, Sven Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics Bayesian parameter inference is one of the key elements for model selection in cosmological research. However, the available inference tools require a large number of calls to simulation codes which can lead to high and sometimes even infeasible computational costs. In this work we propose a new way of emulating simulation codes for Bayesian parameter inference. In particular, this novel approach emphasizes the uncertainty-awareness of the emulator, which allows to state the emulation accuracy and ensures reliable performance. With a focus on data efficiency, we implement an active learning algorithm based on a combination of Gaussian Processes and Principal Component Analysis. We find that for an MCMC analysis of Planck and BAO data on the $Λ$CDM model (6 model and 21 nuisance parameters) we can reduce the number of simulation calls by a factor of $\sim$500 and save about $96\%$ of the computational costs. |
| title | Uncertainty-aware and Data-efficient Cosmological Emulation using Gaussian Processes and PCA |
| topic | Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2307.01138 |