Scientific article
Open access

Model selection as a science driver for dark energy surveys

Published inMonthly notices of the Royal Astronomical Society, vol. 369, no. 4, p. 1725-1734
Publication date2006
First online date2006-06-07

A key science goal of upcoming dark energy surveys is to seek time evolution of the dark energy. This problem is one of {\em model selection}, where the aim is to differentiate between cosmological models with different numbers of parameters. However, the power of these surveys is traditionally assessed by estimating their ability to constrain parameters, which is a different statistical problem. In this paper we use Bayesian model selection techniques, specifically forecasting of the Bayes factors, to compare the abilities of different proposed surveys in discovering dark energy evolution. We consider six experiments -- supernova luminosity measurements by the Supernova Legacy Survey, SNAP, JEDI, and ALPACA, and baryon acoustic oscillation measurements by WFMOS and JEDI -- and use Bayes factor plots to compare their statistical constraining power. The concept of Bayes factor forecasting has much broader applicability than dark energy surveys.

  • Methods: statistical
  • Cosmological parameters
  • Cosmology: theory
Citation (ISO format)
MUKHERJEE, Pia et al. Model selection as a science driver for dark energy surveys. In: Monthly notices of the Royal Astronomical Society, 2006, vol. 369, n° 4, p. 1725–1734. doi: 10.1111/j.1365-2966.2006.10427.x
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Article (Published version)
ISSN of the journal0035-8711

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