http://arxiv.org/abs/2209.01113
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 $H(z)$, in a novel way. These analyses are complemented with two presently debated values of $H_0$, namely, the local measurements by the SH0ES team ($H_0^{\text{R20}} = 73.2 \pm 1.3$~km~Mpc$^{-1}$~s$^{-1}$) and the updated TRGB calibration from the Carnegie Supernova Project ($H_0^{\text{TRGB}} = 69.8 \pm 1.9$~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 $\Lambda$CDM model lies comfortably included at the 1$\sigma$ confidence level for all the examined cases.
P. Mukherjee, J. Said and J. Mifsud
Mon, 5 Sep 22
34/53
Comments: 17 pages, 8 sets of figures
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