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 $\mathrm{\Lambda }\mathrm{CDM}$ 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 $\mathrm{\Lambda }\mathrm{CDM}$ model of $\mathrm{\Delta }{\chi }^2\approx -2.8$ with Planck and BAO and $\mathrm{\Delta }{\chi }^2\approx -7.8$ 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 analyzsing extensions to the $\mathrm{\Lambda }\mathrm{CDM}$ model.

• Received 14 November 2022
• Accepted 10 January 2023

DOI:https://doi.org/10.1103/PhysRevD.107.L021303