https://doi.org/10.1140/epjc/s10052-023-12124-3
Regular Article - Theoretical Physics
Neural network reconstruction of cosmology using the Pantheon compilation
1
Department of Mathematics and Computer Science, Transilvania University of Brasov, Eroilor 29, Brasov, Romania
2
Laboratory of Physics, Faculty of Engineering, Aristotle University of Thessaloniki, 54124, Thessaloníki, Greece
3
Physics and Applied Mathematics Unit, Indian Statistical Institute, 700108, Kolkata, India
4
Institute of Space Sciences and Astronomy, University of Malta, MSD 2080, Msida, Malta
5
Department of Physics, University of Malta, MSD 2080, Msida, Malta
Received:
16
August
2023
Accepted:
8
October
2023
Published online:
24
October
2023
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.
© The Author(s) 2023
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