Cosmological Reconstructions with Artificial Neural Networks
Year: 2021
Type: preprint
Abstract: The relevance of non-parametric reconstructions of cosmological functions lies in the possibility of analyzing the observational data independently of any theoretical model. Several techniques exist and, recently, Artificial Neural Networks have been incorporated to this type of analysis. By using Artificial Neural Networks we present a new strategy to perform non-parametric data reconstructions without any preliminary statistical or theoretical assumptions and even for small observational datasets. In particular, we reconstruct cosmological observables from cosmic chronometers, $f\sigma_8$ measurements and the distance modulus of the Type Ia supernovae. In addition, we introduce a first approach to generate synthetic covariance matrices through a variational autoencoder, for which we employ the covariance matrix of the Type Ia supernovae compilation. To test the usefulness of our developed methods, with the neural network models we generated random data points mostly absent in the original datasets and performed a Bayesian analysis on some simple dark energy models. Some of our findings point out to slight deviations from the $\Lambda$CDM standard model, contrary to the expected values coming from the original datasets.
Source: arXiv (Cornell University)
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3149641726