Testing Gauss-Bonnet Gravity with DESI BAO Data

TL;DR

This paper constrains f(G) gravity models using recent cosmological data, finding both models statistically favored over ΛCDM and revealing unique future transition behaviors.

Contribution

It introduces observational constraints on power-law and exponential f(G) models using DESI BAO data and compares their statistical performance to ΛCDM.

Findings

Both f(G) models are statistically favored over ΛCDM.

The exponential model predicts a future transition to deceleration.

The models fit well with the combined datasets.

Abstract

In the present paper, we observationally constrain f (G) gravity at the background level using Type Ia supernovae from the Pantheon Plus (PP) sample, cosmic chronometer (CC) data, and the recent Baryon Acoustic Oscillation (BAO) measurements released by DESI. For the analysis, we consider two combinations of datasets: (i) PP + CC, and (ii) PP + CC + DESI BAO. In both cases, we determine the best-fit parameters by numerically solving the modified Friedmann equations for two distinct f (G) models, namely the power-law and exponential forms. This is achieved through Markov Chain Monte Carlo (MCMC) simulations. To assess the statistical significance of the f (G) models, we employ both the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). Our results show that both f (G) models are statistically favored over the standard {\Lambda}CDM model. Notably, the exponential model exhibits an additional future transition at redshift closer to -0.1, indicating a possible return to a decelerating phase. This distinctive behavior sets it apart from both the power-law model and the {\Lambda}CDM scenario, which predict continued acceleration into the future.