$f(Q)$-gravity and neutrino physics

TL;DR

This study explores how $f(Q)$-gravity affects cosmological measurements related to neutrinos, providing constraints on neutrino mass and species, and showing that $f(Q)$-gravity models can be statistically favored over standard cosmology.

Contribution

The paper introduces a phenomenological analysis of $f(Q)$-gravity's impact on neutrino-related cosmological observables and constrains neutrino properties using diverse observational data.

Findings

Upper bound on neutrino mass: $oxed{ ext{<}0.277 ext{ eV}}$ at 95\% C.L.

Measured effective neutrino species: $N_{ m eff}=2.93^{+0.31}_{-0.34}$ at 95\% C.L.

$f(Q)+ ext{neutrino mass}$ models are statistically favored over $ ext{Lambda CDM}$.

Abstract

Within the $f(Q)$-gravity framework we perform a phenomenological study of the cosmological observables in light of the degeneracy between neutrinos physics and the modified gravity parameter and we identify specific patterns which allow to break such degeneracy. We also provide separately constraints on the total mass of the neutrinos, $\Sigma m_{\nu}$, and on the effective number of neutrino species, $N_{\rm eff}$, using cosmic microwave background (CMB), baryon acoustic oscillation (BAO), redshift space distortion (RSD), supernovae (SNIa), galaxy clustering (GC) and weak gravitational lensing (WL) measurements. The strongest upper bound on the total mass of the neutrinos is found for the combination of CMB+BAO+RSD+SNIa and it is $\Sigma m_\nu<0.277$ eV at 95\% C.L. For the same combination of data we find $N_{\rm eff}=2.93^{+0.31}_{-0.34}$ at 95\% C.L. We also find that all combinations of data we consider, prefer a stronger gravitational interaction than $\Lambda$CDM. Finally, we consider the $\chi^2$ and deviance information criterion statistics and find the $f(Q)+\Sigma m_\nu$ model to be statistically supported by data over the standard scenario. On the contrary $f(Q)+N_{\rm eff}$ is supported by CMB+BAO+RSD+SNIa but a moderate evidence against it is found with GC and WL data.