https://doi.org/10.1140/epjc/s10052-023-11435-9
Regular Article - Theoretical Physics
Neural network reconstructions for the Hubble parameter, growth rate and distance modulus
1
ICF, Universidad Nacional Autónoma de México, 62210, Cuernavaca, Morelos, Mexico
2
CICATA-Legaria, Instituto Politécnico Nacional, 11500, Mexico City, Mexico
3
Departamento Ingeniería Civil, División de Ingeniería, Universidad de Guanajuato, C.P. 36000, Gto., México
Received:
11
May
2022
Accepted:
26
March
2023
Published online:
19
April
2023
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, 
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.
© The Author(s) 2023
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Funded by SCOAP3. SCOAP3 supports the goals of the International Year of Basic Sciences for Sustainable Development.
