Exploring Interacting Dark Energy with Chaos Quantum-Behaved Particle Swarm Optimization

TL;DR

This paper introduces a novel analytical solution for interacting dark energy models with energy transfer, utilizing a new AI-inspired optimization method, CQPSO, to constrain parameters with astrophysical data and validate results against traditional techniques.

Contribution

First analytical solution for models with energy transfer between dark energy and dark matter, employing CQPSO for parameter estimation and validation.

Findings

Good agreement between CQPSO and MCMC results.

Constraints on energy transfer parameters using astrophysical data.

Validation of CQPSO with simulated scenarios.

Abstract

Models with an interaction between dark energy and dark matter have already been studied for about twenty years. However, in this paper, we provide for the first time a general analytical solution for models with an energy transfer given by $\mathcal{E} = 3H(\xi_1 \rho_c + \xi_2 \rho_d)$. We also use a new set of age-redshift data for 114 old astrophysical objects (OAO) and constrain some special cases of this general energy transfer. We use a method inspired on artificial intelligence, known as Chaos Quantum-behaved Particle Swarm Optimization (CQPSO), to explore the parameter space and search the best fit values. We test this method under a simulated scenario and also compare with previous MCMC results and find good agreement with the expected results.