Neural network reconstruction of cosmology using the Pantheon compilation
Article Type
Research Article
Publication Title
European Physical Journal C
Abstract
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.
DOI
https://10.1140/epjc/s10052-023-12124-3
Publication Date
10-1-2023
Recommended Citation
Dialektopoulos, Konstantinos F.; Mukherjee, Purba; Said, Jackson Levi; and Mifsud, Jurgen, "Neural network reconstruction of cosmology using the Pantheon compilation" (2023). Journal Articles. 3543.
https://digitalcommons.isical.ac.in/journal-articles/3543
Comments
Open Access, Gold