• methods: statistical;
  • cosmology: observations;
  • dark energy


We demonstrate a methodology for optimizing the ability of future dark energy surveys to answer model selection questions, such as ‘Is acceleration due to a cosmological constant or a dynamical dark energy model?’. Model selection figures of merit (FoMs) are defined, exploiting the Bayes factor, and surveys optimized over their design parameter space via a Monte Carlo method. As a specific example, we apply our methods to generic multi-fibre baryon acoustic oscillation spectroscopic surveys, comparable to that proposed for Subaru Measurement of Images and Redshifts Prime Focus Spectrograph, and present implementations based on the Savage–Dickey Density Ratio that are both accurate and practical for use in optimization. It is shown that whilst the optimal surveys using model selection agree with those found using the Dark Energy Task Force (DETF) FoM, they provide better informed flexibility of survey configuration and an absolute scale for performance; for example, we find survey configurations with close-to-optimal model selection performance despite their corresponding DETF FoM being at only 50 per cent of its maximum. This Bayes factor approach allows us to interpret the survey configurations that will be good enough for the task at hand, vital especially when wanting to add extra science goals and in dealing with time restrictions or multiple probes within the same project.