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

Understand

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.