In this work, we reconstruct the Hubble diagram using various data sets, including correlated ones, in Artificial Neural Networks (ANN). Using ReFANN, that was built for data sets with independent uncertainties, we expand it to include non-Guassian data points, as well as data sets with covariance matrices among others. Furthermore, we compare our results with the existing ones derived from Gaussian processes and we also perform null tests in order to test the validity of the concordance model of cosmology.
Cite
@article{arxiv.2305.15499,
title = {Neural network reconstruction of cosmology using the Pantheon compilation},
author = {Konstantinos F. Dialektopoulos and Purba Mukherjee and Jackson Levi Said and Jurgen Mifsud},
journal= {arXiv preprint arXiv:2305.15499},
year = {2023}
}
Comments
11 pages, 12 sets of figures, Accepted for publication in EPJ C