Discovering a new well: Decaying dark matter with profile likelihoods

TL;DR

This study challenges previous Bayesian analyses of decaying dark matter by using profile likelihoods, revealing an intermediate decay regime that slightly improves fit to cosmological data and suggesting decaying dark matter is more plausible than previously thought.

Contribution

The paper introduces profile likelihood methods to analyze decaying dark matter, avoiding volume effects inherent in Bayesian inference, and identifies a viable intermediate decay regime.

Findings

Intermediate decay regime around 3% decays prior to recombination.

Model with additional parameters outperforms standard $\\Lambda$CDM in fit quality.

Decaying dark matter remains a plausible extension to cosmological models.

Abstract

A large number of studies, all using Bayesian parameter inference from Markov Chain Monte Carlo methods, have constrained the presence of a decaying dark matter component. All such studies find a strong preference for either very long-lived or very short-lived dark matter. However, in this letter, we demonstrate that this preference is due to parameter volume effects that drive the model towards the standard $\Lambda$CDM model, which is known to provide a good fit to most observational data. Using profile likelihoods, which are free from volume effects, we instead find that the best-fitting parameters are associated with an intermediate regime where around $3 \%$ of cold dark matter decays just prior to recombination. With two additional parameters, the model yields an overall preference over the $\Lambda$CDM model of $\Delta \chi^2 \approx -2.8$ with \textit{Planck} and BAO and $\Delta \chi^2 \approx -7.8$ with the SH0ES $H_0$ measurement, while only slightly alleviating the $H_0$ tension. Ultimately, our results reveal that decaying dark matter is more viable than previously assumed, and illustrate the dangers of relying exclusively on Bayesian parameter inference when analysing extensions to the $\Lambda$CDM model.