Decaying dark matter with profile likelihoods

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 paper, we demonstrate that this preference is due to parameter volume effects that drive the model towards the standard $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ 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 $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ model of $\mathrm{\ensuremath{\Delta}}{\ensuremath{\chi}}^{2}\ensuremath{\approx}\ensuremath{-}2.8$ with Planck and BAO and $\mathrm{\ensuremath{\Delta}}{\ensuremath{\chi}}^{2}\ensuremath{\approx}\ensuremath{-}7.8$ 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 analyzsing extensions to the $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ model.