Discovering a new well: Decaying dark matter with profile likelihoods (2211.01935v1)

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.