Model-Independent Reconstruction of f(T) Gravity Using Genetic Algorithms

TL;DR

This study employs genetic algorithms to reconstruct the $f(T)$ gravity function in a model-independent manner using cosmological data, revealing a mild deviation from the standard $\

Contribution

It introduces a novel application of genetic algorithms for model-independent $f(T)$ gravity reconstruction from observational data.

Findings

Reconstructed $f(T)$ is consistent with $\

The quadratic form of $f(T)$ is mildly favored over $\

Reconstruction aligns with $\

Abstract

In this paper, we use genetic algorithms, a specific machine learning technique, to achieve a model-independent reconstruction of $f(T)$ gravity. By using $H(z)$ data derived from cosmic chronometers and radial Baryon Acoustic Oscillation method, including the latest Dark Energy Spectroscopic Instrument (DESI) data, we reconstruct the Hubble rate which is the basis parameter for reconstructing $f(T)$ gravity without any assumptions. In this reconstruction process, we use the current value of the Hubble rate, $H_0$, derived by genetic algorithms. The reconstructed $f(T)$ function is consistent with the standard $\Lambda$CDM cosmology within the 1$\sigma$ confidence level across a broad temporal range. The mean $f(T)$ curve, adopting a quadratic form, prompts us to parametrize it using a second degree polynomial. This quadratic deviation from the $\Lambda$CDM scenario is mildly favored by the data.