The detection of the accelerated expansion of the Universe has been one of the major
breakthroughs in modern cosmology. Several cosmological probes (Cosmic Microwave …

We compile a complete collection of reliable Hubble parameter H (z) data to redshift z≤
2.36 and use them with the Gaussian Process method to determine continuous H (z) …

Current observations do not rule out the possibility that the Universe might end up in an
abrupt event, but that fatality might be avoided, as it happens in the pseudorip dark energy …

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 prospect of nonparametric reconstructions of cosmological parameters from
observational data sets has been a popular topic in the literature for a number of years. This …

In this work, we explore the possibility of using artificial neural networks to impose
constraints on teleparallel gravity and its f (T) extensions. We use the available Hubble …

In the context of a Hubble tension problem that is growing in its statistical significance, we
reconsider the effectiveness of non-parametric reconstruction techniques which are …

In this paper, we present an analysis of Supernova Ia (SNIa) distance moduli (μ (z)) and dark
energy using an Artificial Neural Network (ANN) reconstruction based on LSST simulated …

The applications of artificial neural networks in the cosmological field have shone
successfully during the past decade, this is due to their great ability of modeling large …

We reconstruct the neutrino mass as a function of redshift, z, from current cosmological data
using both standard binned priors and linear spline priors with variable knots. Using cosmic …