Non-Linear Matter Power Spectrum Modeling in Interacting Dark Energy Cosmologies

TL;DR

This paper develops a refined model for the non-linear matter power spectrum in interacting dark energy-dark matter cosmologies, using simulations and theoretical fitting, to improve constraints on the coupling parameter with upcoming surveys.

Contribution

It introduces a new parametric fit and a modified halo model for IDE cosmologies, achieving high accuracy and enabling better observational constraints on dark sector interactions.

Findings

Achieved 5% accuracy in fitting simulated spectra

Refined halo model fits down to 1 h/Mpc scales

Forecasted Euclid survey constraints on coupling parameter, σ(ξ)=0.0110

Abstract

Understanding the behavior of the matter power spectrum on non-linear scales beyond the $\Lambda$CDM model is crucial for accurately predicting the large-scale structure (LSS) of the Universe in non-standard cosmologies. In this work, we present an analysis of the non-linear matter power spectrum within the framework of interacting dark energy-dark matter cosmologies (IDE). We employ N-body simulations and theoretical models to investigate the impact of IDE on these non-linear scales. Beginning with N-body simulations characterized by a fixed parameter space delineated by prior observational research, we adeptly fit the simulated spectra with a simple parametric function, achieving accuracy within 5\%. Subsequently, we refine a modified halo model tailored to the IDE cosmology, exhibiting exceptional precision in fitting the simulations down to scales of approximately 1 h/Mpc. To assess the model's robustness, we conduct a forecast analysis for the Euclid survey, employing our refined model. We find that the coupling parameter $\xi$ will be constrained to $\sigma(\xi) = 0.0110$. This marks a significant improvement by an order of magnitude compared to any other current observational tests documented in the literature. These primary findings pave the way for a novel preliminary approach, enabling the utilization of IDE models for observational constraints concerning LSS data on non-linear scales.