Optimizing future dark energy surveys for model selection goals
  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 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 SuMIRe PFS,
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) Figure of Merit, 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 Figure of Merit being at only 50%
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.
