http://arxiv.org/abs/1910.03636
In this work, we propose a new non-parametric approach for reconstructing a function from observational data using Artificial Neural Network (ANN), which has no assumptions to the data and is a completely data-driven approach. We test the ANN method by reconstructing functions of the Hubble parameter measurements $H(z)$ and the distance redshift relation $D_L(z)$ of type Ia supernova. We find that both $H(z)$ and $D_L(z)$ can be reconstructed with high accuracy. Furthermore, we estimate cosmological parameters using the reconstructed functions of $H(z)$ and $D_L(z)$ and find the results are consistent with those obtained using the observational data directly. Therefore, we propose that the function reconstructed by ANN can represent the actual distribution of observational data and can be used for parameter estimation in further cosmological research. In addition, we present a new strategy to train and evaluate the neural network, and a code for reconstructing functions using ANN has been developed and will be available soon.
G. Wang, X. Ma, S. Li, et. al.
Thu, 10 Oct 19
50/63
Comments: 12 pages, 13 figures and 1 table