Cosmological perturbations on an averaged background

TL;DR

This paper develops a covariant, gauge-invariant framework to analyze how inhomogeneities and backreaction influence the growth of large-scale structures in cosmology, emphasizing the importance of these effects for accurate modeling.

Contribution

It introduces an effective fluid approach to incorporate backreaction effects into linear perturbation theory in averaged cosmological models.

Findings

Backreaction can significantly affect linear structure growth.

Neglecting backreaction may bias predictions of large-scale structure development.

The formalism is applied to models fitting observational data well.

Abstract

In relativistic cosmology, the formation of nonlinear inhomogeneities can induce non-negligible backreaction on late-time expansion. Among the important consequences for precision cosmology is the potential impact on the linear growth of large-scale structures. We address this impact by combining covariant spatial averaging with covariant and gauge-invariant perturbation theory. We focus on irrotational dust model spacetimes. The effects of backreaction and nontrivial dynamical curvature on the average cosmological dynamics are formulated as the addition of an effective perfect fluid with pressure. We then introduce an effective background driven by both the averaged dust density and the emergent effective fluid, and derive the general evolution equations for linear perturbations of this system. The residual freedom in this framework amounts to specifying the properties of the effective-fluid perturbations as a closure condition. We analyse two physically motivated choices for this condition. In addition, we clarify the conditions under which the coupling between linear structure growth and perturbations of the effective fluid can be neglected. Finally, we apply this formalism to four examples of averaged cosmological models from the literature, three of which -- intended as effective full descriptions of the largest scales -- have been shown to provide a good fit to observational data. Our results highlight the importance of backreaction effects in shaping linear structure growth in such models. Neglecting these effects may thus lead to biased predictions for the development of large structures, even when the models provide a good description of the general background observables.