• DOI:10.1214/08-AOAS229
• Corpus ID: 17040384

Inference for the dark energy equation of state using Type IA supernova data

@article{Genovese2008InferenceFT,
  title={Inference for the dark energy equation of state using Type IA supernova data},
  author={Christopher R. Genovese and Peter E. Freeman and Larry A. Wasserman and Robert C. Nichol and Christopher J. Miller},
  journal={The Annals of Applied Statistics},
  year={2008},
  volume={3},
  pages={144-178}
}

• C. Genovese, P. Freeman, +2 authors C. Miller
• Published 27 May 2008
• Physics
• The Annals of Applied Statistics

The surprising discovery of an accelerating universe led cosmologists to posit the existence of “dark energy”—a mysterious energy field that permeates the universe. Understanding dark energy has become the central problem of modern cosmology. After describing the scientific background in depth, we formulate the task as a nonlinear inverse problem that expresses the comoving distance function in terms of the dark-energy equation of state. We present two classes of methods for making sharp statistical inferences about the equation of state from observations of Type Ia Supernovae (SNe). First, we derive a technique for testing hypotheses about the equation of state that requires no assumptions about its form and can distinguish among competing theories. Second, we present a framework for computing parametric and nonparametric estimators of the equation of state, with an associated assessment of uncertainty. Using our approach, we evaluate the strength of statistical evidence for various competing models of dark energy. Consistent with current studies, we find that with the available Type Ia SNe data, it is not possible to distinguish statistically among popular dark-energy models, and that, in particular, there is no support in the data for rejecting a cosmological constant. With much more supernova data likely to be available in coming years (e.g., from the DOE/NASA Joint Dark Energy Mission), we address the more interesting question of whether future data sets will have sufficient resolution to distinguish among competing theories.