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

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.

Published at NeurIPS 2021 workshop: Bayesian Deep Learning