Decaying dark matter with profile likelihoods

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 ACDM 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 ACDM model of delta chi(2 )asymptotic to -2.8 with Planck and BAO and delta chi(2 )asymptotic to -7.8 with the SH0ES H(0 )measurement, while only slightly alleviating the H0 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 ACDM model.