Constraining dark energy models using Jackknife and Bootstrap resampling

TL;DR

This paper applies Jackknife and Bootstrap resampling methods to supernova data to estimate dark energy model parameters, providing an alternative to Bayesian techniques and highlighting data limitations.

Contribution

It introduces resampling techniques for dark energy analysis, offering a complementary approach to Bayesian methods and revealing data limitations affecting parameter estimates.

Findings

Jackknife indicates a strong positive correlation between h and M.

Resampling reveals higher uncertainties in parameter estimates.

Results suggest potential implications for the Hubble tension.

Abstract

Analyses of type Ia supernovae have helped us shed light on the existence and nature of dark energy. Most of these analyses have relied on Bayesian techniques. In this work, we rely on resampling techniques to analyse supernova data. In particular, we use the generalised least squares method together with Jackknife and Bootstrap techniques to estimate parameters of $\Lambda$CDM, flat $\Lambda$CDM, $w$CDM, flat $w$CDM, and flat $w_0\,w_a$CDM models from the recent PantheonPlus and SH0ES data. For completeness, we also perform Bayesian analysis using Markov chain Monte Carlo (MCMC) and nested sampling algorithms, and compare the results. We note that resampling techniques can help highlight the limitations of the data. For instance, we see that the Jackknife method estimates a strong positive correlation between $h$ and $M$ and higher standard deviations for both. This may have significant implications for the Hubble tension. We conclude with a discussion of our results.