Two Component Model of Dark Energy

TL;DR

This paper explores a two-component dark energy model, fitting it to supernova and gamma-ray burst data using MCMC, and compares its performance to standard models, finding it can fit current data well and sometimes better.

Contribution

It introduces and tests a two-component dark energy model, demonstrating its viability and potential advantages over traditional single-component models.

Findings

Two-component models fit current data reasonably well.

In some cases, two-component models outperform standard models.

The model's fit depends on data sets and selection criteria.

Abstract

We consider the possibility that the dark energy is made up of two or more independent components, each having a different equation of state. We fit the model with supernova and gamma-ray burst (GRB) data from resent observations, and use the Markov Chain Monte Carlo (MCMC) technique to estimate the allowed parameter regions. We also use various model selection criteria to compare the two component model with the LCDM, one component dark energy model with static or variable w(XCDM), and with other multi-component models. We find that the two component models can give reasonably good fit to the current data. For some data sets, and depending somewhat on the model selection criteria, the two component model can give better fit to the data than XCDM with static w and XCDM with variable w parameterized by w = w_0 + w_az/(1+z).