Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/121114
Title: | Neural network reconstruction of H0 (z) and its application in teleparallel gravity |
Authors: | Mukherjee, Purba Said, Jackson Mifsud, Jurgen |
Keywords: | Cosmology -- Observations Neural networks (Computer science) Gravity -- Research Dark energy (Astronomy) Gravitation |
Issue Date: | 2022 |
Publisher: | Institute of Physics Publishing Ltd. |
Citation: | Mukherjee, P., Said, J. L., & Mifsud, J. (2022). Neural network reconstruction of H'(z) and its application in teleparallel gravity. Journal of Cosmology and Astroparticle Physics, 2022(12), 1-26. |
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 H0 to assess their impact on the analysis, which are the local measurements by the SH0ES team (HR200=73.2±1.3 km Mpc−1 s−1) and the updated TRGB calibration from the Carnegie Supernova Project (HTRGB0=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. |
URI: | https://www.um.edu.mt/library/oar/handle/123456789/121114 |
ISSN: | 14757516 |
Appears in Collections: | Scholarly Works - InsSSA |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Neural_network_reconstruction_of_H0_(z)_and_its_application_in_teleparallel_gravity(2022).pdf Restricted Access | 1.84 MB | Adobe PDF | View/Open Request a copy |
Items in OAR@UM are protected by copyright, with all rights reserved, unless otherwise indicated.