How Complex is Dark Energy? A Bayesian Analysis of CPL Extensions with Recent DESI BAO Measurements
Mohammad Malekjani, Saeed Pourojaghi, and Zahra Davari

TL;DR
This paper uses Bayesian analysis of recent cosmological data to compare dark energy models, finding that simple two-parameter models are sufficient and more complex extensions are not justified by current observations.
Contribution
It provides a Bayesian comparison showing that standard CPL and certain two-parameter dark energy models are favored over more complex extensions, supporting simpler dynamical dark energy parametrizations.
Findings
CPL is preferred over wCDM, indicating evolving dark energy.
Higher-order CPL extensions are not favored by data.
Two-parameter models like w0 + wb(1-a)^2 fit data well.
Abstract
The nature of dark energy is one of the big puzzling issues in cosmology. While CDM provides a good fit to the observational data, evolving dark energy scenarios, such as the CPL parametrization, offer a compelling alternative. In this paper, we present a Bayesian model comparison of various dark energy parametrizations using a joint analysis of Cosmic Microwave Background data, DESI Baryon Acoustic Oscillation measurements, and the PantheonPlus (or Union3) Supernovae type Ia sample. We find that while the CDM model is initially favored over a constant CDM model, the CPL parametrization is significantly preferred over CDM, reinforcing recent evidence for an evolving dark energy component, consistent with DESI collaboration findings. Crucially, when testing higher-order CPL extensions, the so-called CPL and CPL, our Bayesian analysis shows that the…
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