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Constraining f(G) gravity models using MCMC method
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DOI：10.1016/j.dark.2026.102246.png)
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Abstract
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In the present paper, we explore the viability of three models in Gauss-Bonnet f(G) gravity using data from different surveys including Plank data, Pantheon plus data, Baryonic acoustic oscillation data and DESI data. We start by reconstructing modified Friedmann equation for each model and by solving numerically the obtained equations, we assess the fitness of the evolution of Hubble parameter with data. After checking the fitness of each model, we employ Markov Chain Monte Carlo (MCMC) analysis to extract the best-fit parameters for each model. For the exponential model, we get H0=69.03±1.01 and Ωm=0.302±0.017 and for the logarithmic model H0=69.20±0.90 and Ωm=0.3004±0.0144, while H0=69.23±0.92 and Ωm=0.301±0.015 for the trigonometric model. To assess the statistical significance of the considered models, we introduce the Akaike Information Criterion (AIC), Deviation Information Criterion (DIC) and the Bayesian Information Criterion (BIC). Our analysis suggests that the best approximation for describing the late-time acceleration of the universe is represented by a scenario with the trigonometric model (model I) and exponential model (model II), indicating that both f(G) gravity models can effectively reproduce late-time cosmic acceleration without dark energy.
Keywords:
f(G) gravity
Gauss-Bonnet
cosmic acceleration
Markov Chain Monte Carlo
model comparison
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