Structure growth in $f(Q)$ cosmology

TL;DR

This paper investigates the growth of large-scale structures in the universe within $f(Q)$ gravity, combining expansion history and growth data to constrain cosmological parameters and assess the model's viability.

Contribution

It introduces a comprehensive analysis of structure growth in $f(Q)$ gravity using observational data and MCMC methods, providing new constraints on model parameters.

Findings

Constraints on $oldsymbol{oldsymbol{oldsymbol{ ext{Ω}}_m}}$, $oldsymbol{oldsymbol{oldsymbol{ ext{σ}}_8}}$, and $oldsymbol{n}$ from combined data.

Assessment of $f(Q)$ gravity's fit to growth and expansion data using AIC and BIC criteria.

Demonstration of the model's consistency with observational measurements of structure growth.

Abstract

We take into account redshift-space distortion measurements to investigate the growth of cosmological large-scale structures within the framework of modified symmetric teleparallel $f(Q)$ gravity. After comparing the predictions of the $f(Q)$-gravity expansion history with OHD and SNIa Pantheon+ sample datasets and constraining the pertinent cosmological parameters $\Omega_{m}$ and $H_0$, together with the exponent $n$ for $f(Q)$ power-law models, we derive the full system of equations governing linear cosmological perturbations to study matter fluctuations using the $1 + 3$ covariant formalism when applied to $f(Q)$ gravity. Thus, we resort to both the usual redshift-space distortion data $f\sigma_8$ and some recent separate measurements of the growth rate $f$ and the amplitude of matter fluctuations $\sigma_8$ from the VIPERS and SDSS collaborations to find the best-fit cosmological parameters $\Omega_m$, $\sigma_{8}$ and $n$. We also apply a collective analysis of such growth-structure data together with the aforementioned cosmic expansion measurements, to restrict these parameters through Monte Carlo Markov Chain simulations. determining the statistical significance for the best-fit parameter values through the AIC and BIC Bayesian selection criteria.