Constraining cosmological parameters from N-body simulations with Bayesian Neural Networks

• URL: http://arxiv.org/abs/2112.11865v1
• Date: Wed, 22 Dec 2021 13:22:30 GMT
• Title: Constraining cosmological parameters from N-body simulations with Bayesian Neural Networks
• Authors: Hector J. Hortua
• Abstract summary: We use The Quijote simulations in order to extract the cosmological parameters through Bayesian Neural Networks. This kind of model has a remarkable ability to estimate the associated uncertainty, which is one of the ultimate goals in the precision cosmology era.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we use The Quijote simulations in order to extract the cosmological parameters through Bayesian Neural Networks. This kind of model has a remarkable ability to estimate the associated uncertainty, which is one of the ultimate goals in the precision cosmology era. We demonstrate the advantages of BNNs for extracting more complex output distributions and non-Gaussianities information from the simulations.

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