Designing Decisive Detections
  We present a general Bayesian formalism for the definition of Figures of
Merit (FoMs) quantifying the scientific return of a future experiment. We
introduce two new FoMs for future experiments based on their model selection
capabilities, called the decisiveness of the experiment and the expected
strength of evidence. We illustrate these by considering dark energy probes,
and compare the relative merits of stage II, III and IV dark energy probes. We
find that probes based on supernovae and on weak lensing perform rather better
on model selection tasks than is indicated by their Fisher matrix FoM as
defined by the Dark Energy Task Force. We argue that our ability to optimize
future experiments for dark energy model selection goals is limited by our
current uncertainty over the models and their parameters, which is ignored in
the usual Fisher matrix forecasts. Our approach gives a more realistic
assessment of the capabilities of future probes and can be applied in a variety
of situations.
