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Neural network reconstruction of cosmology using the Pantheon compilation

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arxiv 2305.15499 v2 pith:PTL2HDV4 submitted 2023-05-24 gr-qc astro-ph.COcs.LG

Konstantinos F. Dialektopoulos , Purba Mukherjee , Jackson Levi Said , Jurgen Mifsud This is my paper · ORCID
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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.

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  1. Cosmo-Learn: code for learning cosmology using different methods and mock data

    astro-ph.CO 2025-08 conditional novelty 5.0 of 10

    An open-source toolkit that simulates late-universe cosmological observations and benchmarks MCMC, genetic algorithms, Gaussian processes, Bayesian ridge regression, and neural networks in one pipeline.

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