Investigating Interacting Dark Energy Models Using Fast Radio Burst Observations

TL;DR

This study explores how Fast Radio Burst observations can be used to constrain interacting dark energy models, providing new insights into cosmological parameters and the cosmic coincidence problem.

Contribution

It introduces a comprehensive analysis of three interacting dark energy models using FRB data and simulations, demonstrating their potential to improve cosmological constraints.

Findings

FRB data constrains key cosmological parameters.

The gamma_m IDE model may alleviate the cosmic coincidence problem.

xi IDE model shows slightly better model comparison metrics.

Abstract

This paper investigates the utility of Fast Radio Bursts (FRBs) as novel observational probes to constrain models of interacting dark energy (IDE). By leveraging FRB dispersion measures (DMs) and redshifts, we perform a comprehensive analysis of three IDE models: gamma_m IDE, gamma_x IDE, and xi IDE, using Markov Chain Monte Carlo (MCMC) methods based on 86 localized FRBs and simulated datasets containing 2500 to 10000 mock events. By disentangling the contributions to the observed DMs from the Milky Way, host galaxies, and the intergalactic medium (IGM), key cosmological parameters are constrained, including the Hubble constant (H0), matter density (Omega_m), the dark energy equation of state (omega_x), and interaction strengths (gamma_m, gamma_x, xi). The best-fit values of the gamma_m IDE model indicate a potential alleviation of the cosmic coincidence problem. Subsequently, we utilize information criteria (IC) to conduct a comparative assessment of the three IDE models. When applied to the current sample of observed FRBs, the xi IDE model yields slightly lower IC values than the gamma_m IDE and gamma_x IDE models across all three criteria, although the differences are not statistically significant. These results underscore the value of FRB measurements as complementary probes that provide further constraints on alternative cosmological models.