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

2021/12/22 by Héctor J. Hortúa, Hortua, Hector J.
Computer Science · Engineering · Mathematics · #Control Systems and Identification #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #FOS: Computer and information sciences #FOS: Physical sciences #Gaussian Processes and Bayesian Inference #Machine Learning (stat.ML) #Statistical and numerical algorithms

paper · pdf · doi:10.48550/arxiv.2112.11865

openalex publication_date 2021/12/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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