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• W4366401421 abstract "This paper introduces a new approach to reconstruct cosmological functions using artificial neural networks based on observational measurements with minimal theoretical and statistical assumptions. By using neural networks, we can generate computational models of observational datasets, and then we compare them with the original ones to verify the consistency of our method. This methodology is applicable to even small-size datasets. In particular, we test the proposed method with data coming from cosmic chronometers, $fsigma_8$ measurements, and the distance modulus of the Type Ia supernovae. Furthermore, we introduce a first approach to generate synthetic covariance matrices through a variational autoencoder, using the systematic covariance matrix of the Type Ia supernova compilation." @default.
• W4366401421 created "2023-04-21" @default.
• W4366401421 creator A5007983349 @default.
• W4366401421 creator A5016483903 @default.
• W4366401421 creator A5057140035 @default.
• W4366401421 creator A5057995490 @default.
• W4366401421 date "2023-04-19" @default.
• W4366401421 modified "2023-10-13" @default.
• W4366401421 title "Neural network reconstructions for the Hubble parameter, growth rate and distance modulus" @default.
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