Bayesian and Machine-Learning Analyses of Nonminimal $f(Q)$ Gravity and $H_0$ Tension

Simran Arora; Mridul Patel

doi:10.1016/j.jheap.2026.100682

paper

Bayesian and Machine-Learning Analyses of Nonminimal $f (Q)$ Gravity and $H_{0}$ Tension

arXiv:2511.06332 · doi:10.1016/j.jheap.2026.100682

Abstract

In this study, the cosmological implications of nonminimally coupled $f (Q)$ gravity are examined within the metric-affine formalism, in which the nonmetricity scalar $Q$ couples directly to the matter Lagrangian. Within the symmetric teleparallel framework, a representative $f (Q)$ model is constructed, and the corresponding background cosmological equations are derived. The analysis aims to test whether this geometric formulation yields more consistent realizations of nonminimal matter-geometry couplings. A comprehensive statistical MCMC analysis is performed using cosmic chronometers, DESI BAO DR2, and Type~Ia supernovae from the Pantheon+, DESY5, and Union3 samples and CMB. To complement the statistical study, we employ machine learning methods, such as linear regression, support vector regression (SVR), and random forest algorithms, to evaluate the predictive performance and robustness of the data. The results indicate that a partial alleviation of the $H_{0}$ tension can be achieved for a broad range of parameter choices. Nonetheless, $f (Q)$ gravity emerges as a promising and flexible framework for late-time cosmology, motivating further exploration of extended models consistent with all observations.

arXiv comment15 pages and 7 figures. Published in the Journal of High Energy Astrophysics (JHEAP)

Bayesian and Machine-Learning Analyses of Nonminimal f(Q) Gravity and H0​ Tension

Abstract

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Bayesian and Machine-Learning Analyses of Nonminimal $f (Q)$ Gravity and $H_{0}$ Tension