Cosmic Bayes: Datasets and priors in the hunt for dark energy

Bayesian methods are ubiquitous in contemporary observational cosmology. They enter into three main tasks: (I) cross-checking datasets for consistency; (II) fixing constraints on cosmological parameters; and (III) model selection. This article explores some epistemic limits of using Bayesian methods. The first limit concerns the degree of informativeness of the Bayesian priors and an ensuing methodological tension between task (I) and task (II). The second limit concerns the choice of wide flat priors and related tension between (II) parameter estimation and (III) model selection. The Dark Energy Survey (DES) and its recent Year 1 results illustrate both these limits concerning the use of Bayesianism.