Figures of merit for present and future dark energy probes
Michael J. Mortonson (CCAPP/Ohio State), Dragan Huterer (Michigan),, Wayne Hu (KICP/UChicago)

TL;DR
This paper evaluates how future supernova and CMB data will significantly improve constraints on dark energy models, especially when using principal component analysis, reducing the allowed parameter space by up to two orders of magnitude.
Contribution
It introduces a volume-based figure of merit for dark energy constraints and demonstrates substantial improvements with future data, especially for models with multiple parameters.
Findings
Future data can reduce the dark energy parameter space volume by a factor of ~100.
Constraints on a two-parameter dark energy model improve by a factor of 10.
Principal component analysis reveals that 2-3 parameters suffice to describe typical quintessence models.
Abstract
We compare current and forecasted constraints on dynamical dark energy models from Type Ia supernovae and the cosmic microwave background using figures of merit based on the volume of the allowed dark energy parameter space. For a two-parameter dark energy equation of state that varies linearly with the scale factor, and assuming a flat universe, the area of the error ellipse can be reduced by a factor of ~10 relative to current constraints by future space-based supernova data and CMB measurements from the Planck satellite. If the dark energy equation of state is described by a more general basis of principal components, the expected improvement in volume-based figures of merit is much greater. While the forecasted precision for any single parameter is only a factor of 2-5 smaller than current uncertainties, the constraints on dark energy models bounded by -1<w<1 improve for…
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