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- W4308244958 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." @default.
- W4308244958 created "2022-11-09" @default.
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- W4308244958 date "2022-11-03" @default.
- W4308244958 modified "2023-09-27" @default.
- W4308244958 title "Discovering a new well: Decaying dark matter with profile likelihoods" @default.
- W4308244958 doi "https://doi.org/10.48550/arxiv.2211.01935" @default.
- W4308244958 hasPublicationYear "2022" @default.
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