TY - JOUR
T1 - Neural network reconstructions for the Hubble parameter, growth rate and distance modulus
AU - Gómez-Vargas, Isidro
AU - Medel-Esquivel, Ricardo
AU - García-Salcedo, Ricardo
AU - Vázquez, J. Alberto
N1 - Publisher Copyright:
© 2023, The Author(s).
PY - 2023/4
Y1 - 2023/4
N2 - 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, fσ8 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.
AB - 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, fσ8 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.
UR - http://www.scopus.com/inward/record.url?scp=85153228464&partnerID=8YFLogxK
U2 - 10.1140/epjc/s10052-023-11435-9
DO - 10.1140/epjc/s10052-023-11435-9
M3 - Artículo
AN - SCOPUS:85153228464
SN - 1434-6044
VL - 83
JO - European Physical Journal C
JF - European Physical Journal C
IS - 4
M1 - 304
ER -