Neural network reconstruction of H'(z) and its application in teleparallel gravity

Abstract

Abstract In this work, we explore the possibility of using artificial neural networks to impose constraints on teleparallel gravity and its f(T) extensions. We use the available Hubble parameter observations from cosmic chronometers and baryon acoustic oscillations from different galaxy surveys. We discuss the procedure for training a network model to reconstruct the Hubble diagram. Further, we describe the procedure to obtain H'(z), the first order derivative of H(z), using artificial neural networks which is a novel approach to this method of reconstruction. These analyses are complemented with further studies on the impact of two priors which we put on H 0 to assess their impact on the analysis, which are the local measurements by the SH0ES team (H 0 R20 = 73.2 ± 1.3 km Mpc-1 s-1) and the updated TRGB calibration from the Carnegie Supernova Project (H 0 TRGB = 69.8 ± 1.9 km Mpc-1 s-1), respectively. Additionally, we investigate the validity of the concordance model, through some cosmological null tests with these reconstructed data sets. Finally, we reconstruct the allowed f(T) functions for different combinations of the observational Hubble data sets. Results show that the ΛCDM model lies comfortably included at the 1σ confidence level for all the examined cases.