Cosmological constraints on non-adiabatic dark energy perturbations

TL;DR

This paper investigates the effects of non-adiabatic dark energy perturbations on cosmological observables, providing analytical solutions, numerical validation, and constraints from current data, finding no significant non-adiabatic component.

Contribution

It introduces a comprehensive analysis of non-adiabatic dark energy perturbations, including analytical solutions, numerical validation, and observational constraints, which was not extensively explored before.

Findings

Non-adiabatic pressure perturbation amplitude is consistent with zero within 1σ.

Analytical solutions agree with numerical and Boltzmann code results to below 1%.

New RSD likelihood for MontePython based on Gold 2018 data is presented.

Abstract

The exact nature of dark energy is currently unknown and its cosmological perturbations, when dark energy is assumed not to be the cosmological constant, are usually modeled as adiabatic. Here we explore the possibility that dark energy might have a nonadiabatic component and we examine how it would affect several key cosmological observables. We present analytical solutions for the growth rate and growth index of matter density perturbations and compare them to both numerical solutions of the fluid equations and an implementation in the Boltzmann code CLASS, finding that they all agree to well below one percent. We also perform a Monte Carlo analysis to derive constraints on the parameters of the nonadiabatic component using the latest cosmological data, including the temperature and polarization spectra of the Cosmic Microwave Background as observed by Planck, the Baryon Acoustic Oscillations, the Pantheon type Ia supernovae compilation and lastly, measurements of Redshift Space Distortions (RSD) of the growth rate of matter perturbations. We find that the amplitude of the nonadiabatic pressure perturbation is consistent with zero within $1\sigma$. Finally, we also present a new, publicly available, RSD likelihood for MontePython based on the "Gold 2018" growth rate data compilation.