Standard Perturbation Theory for Interacting Dark Sector cosmologies I: Breakdown of Einstein de Sitter kernels

TL;DR

This paper develops a time-dependent perturbation theory framework for interacting dark sector models, revealing that neglecting kernel evolution leads to significant inaccuracies in nonlinear structure predictions.

Contribution

It introduces a non-EdS perturbation theory approach for IDS cosmologies, accounting for the time dependence of kernels due to dark sector interactions.

Findings

Non-trivial time dependence in perturbative kernels caused by dark sector interactions.

Deviations in the matter power spectrum exceed percent level even for small interactions.

Neglecting kernel evolution biases predictions on mildly nonlinear scales.

Abstract

Interacting dark sector (IDS) models provide a commonly explored extension of the standard $\Lambda$CDM cosmology, allowing for non-gravitational energy--momentum exchange between cold dark matter (CDM) and dark energy (DE). Although such models can be constructed to reproduce the same background expansion history as $\Lambda$CDM, their impact on the growth of cosmic structures is fundamentally different and requires a careful treatment of cosmological perturbations. In this work, we develop the one-loop Standard Perturbation Theory (SPT) formalism for IDS cosmologies without invoking the Einstein--de~Sitter (EdS) approximation. We show that even weak dark sector interactions induce a non-trivial time dependence in the perturbative kernels, leading to a breakdown of the EdS approximation commonly assumed in $\Lambda$CDM analyses. By deriving and numerically solving the evolution equations for the second- and third-order kernels, we compute the corresponding one-loop corrections to the matter power spectrum and find that the resulting deviations can significantly exceed the percent level, even for small interaction strengths. Our results demonstrate that nonlinear corrections are systematically enhanced in IDS models and that neglecting the full time dependence of the kernels can lead to biased predictions on mildly nonlinear scales. These findings establish the necessity of a time-dependent perturbative treatment for IDS scenarios and provide a robust framework for precision tests using nonlinear large-scale structure (LSS) observables.