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

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.

LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring Its Applications

“…Neural networks, despite being more performant, do not natively model d L ¢ and its associated error d L s ¢. Although attempts to numerically differentiate the network outputs for d L have been undertaken (Mukherjee et al 2022;Dialektopoulos et al 2023Dialektopoulos et al , 2024, these have led to substantially large uncertainties, rendering final results ineffective when it comes to precision cosmology at higher redshifts. Experiments with LADDER have shown emerging possibilities through potential applications demonstrated in the current study.…”

A thorough investigation of the prospects of eLISA in addressing the Hubble tension: Fisher forecast, MCMC and Machine Learning

“…This entails the need for faster and more efficient computational tools and data handling algorithms. Besides conventional methods of simulation and data analysis, various machine learning (ML) techniques like Gaussian Processes (GP), Genetic Algorithms (GA), and various deep learning algorithms are increasingly being used in different areas of cosmology (for a small body of diverse examples from recent years see [60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78]). Gaussian Processes, for example, have already found considerable application in the area of non-parametric reconstructions of various cosmological parameters [79][80][81][82].…”

A thorough investigation of the prospects of eLISA in addressing the Hubble tension: Fisher Forecast, MCMC and Machine Learning

“…This entails the need for faster and more efficient computational tools and data handling algorithms. Besides conventional methods of simulation and data analysis, various Machine Learning (ML) techniques like Gaussian Processes (GP), Genetic Algorithms (GA), and various deep learning algorithms are increasingly being used in different areas of cosmology (for a small body of diverse examples from recent years see [58][59][60][61][62][63][64][65][66][67][68][69][70][71][72][73]). Gaussian Processes, for example, have already found considerable application in the area of non-parametric reconstructions of various cosmological parameters [74][75][76][77].…”