A deep learning approach to cosmological dark energy models

Understand

We propose a novel deep learning tool in order to study the evolution of dark energy models.

• The aim is to combine two architectures: the Recurrent Neural Networks (RNN) and the Bayesian Neural Networks (BNN), we named this full network as RNN+BNN.
• The first one is capable of learning complex sequential information to classify objects like supernovae and use the light-curves directly to learn information from the sequence of observations.
• Since RNN is not capable to calculate the uncertainties, BNN emerges as a solution for problems in deep learning like, for example, the overfitting.