Abstract
AbstractIn 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.
Funder
European Cooperation in Science and Technology
Hellenic Foundation for Research and Innovation
Publisher
Springer Science and Business Media LLC
Subject
Physics and Astronomy (miscellaneous),Engineering (miscellaneous)
Cited by
3 articles.
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