Applying the BF method on the DESI evidence for dynamical dark energy models

TL;DR

This paper introduces a hybrid Bayesian-frequentist method to compare dynamical dark energy models using DESI data, reducing prior dependence and providing robust model preference assessments.

Contribution

It applies the BF method to DESI data, integrating Bayesian and frequentist approaches for more reliable model comparison of dark energy models.

Findings

Weak priors favor wCDM; strong priors favor CPL.

The BF method shows CPL preference across priors, with reduced impact.

Current data is insufficient to decisively distinguish models.

Abstract

Recent baryon acoustic oscillation measurements from the DESI, when combined with CMB data and Type Ia supernovae observations, indicate a preference for dynamical dark energy when considering the Chevallier-Polarski-Linder (CPL) model, over the standard {\Lambda}CDM or the wCDM model. However, the Bayes factor, a key metric for model comparison, remains inconclusive on which model is preferred. This paper applies the BF method, that integrates both Bayesian and frequentist approaches to DESI data to address the limitations of purely frequentist or Bayesian methods. It consists in considering the Bayes factor as a random variable and calculates its distribution, that results from values computed in a frequentist approach after perturbing the data following the model considered. We apply this hybrid method to DESI data, comparing the CPL and w models under various prior conditions, including weak and strong priors, and theory-informed priors. We find that, when the traditional bayes factor is considered, that weak priors favor the w model over CPL, while strong priors favor CPL. Additionally, theory-informed priors further enhance the preference for the w model. While when we apply the BF method, the preference for CPL over w is seen in all cases albeit with similar but reduced impact on the p-value from the different prior considerations. We also tried to generalize further, by perturbing as well the covariance matrix following the model considered, and found that, in general, the current data in that case is not stringent enough to disentangle between the two models. Our results demonstrate that varying the Bayes factor as a random variable, providing that the covariance matrix is kept as model independent, provides a robust model comparison, reducing the impact of prior dependence as well as offering quantitative assessment of the preferences of the competing models.(abridged)