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Model selection as a science driver for dark energy surveys

Mukherjee, Pia ; Parkinson, David ; Corasaniti, Pier Stefano ; Liddle, Andrew R. ; Kunz, Martin

In: Monthly Notices of the Royal Astronomical Society, 2006, vol. 369, no. 4, p. 1725-1734

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    Title
    • Model selection as a science driver for dark energy surveys
    Author
    • Mukherjee, Pia. Astronomy Centre, University of Sussex, Brighton BN1 9QH
    • Parkinson, David. Astronomy Centre, University of Sussex, Brighton BN1 9QH
    • Corasaniti, Pier Stefano. ISCAP, Columbia University, New York, NY 10027, USA
    • Liddle, Andrew R.. Astronomy Centre, University of Sussex, Brighton BN1 9QH
    • Kunz, Martin. Department of Theoretical Physics, University of Geneva, 24 quai Ernest Ansermet, CH-1211 Geneva 4, Switzerland
    Document Type
    • Postprint
    OAI-PMH Identifier
    • oai:doc.rero.ch:297000
    Summary
    A key science goal of upcoming dark energy surveys is to seek time-evolution of the dark energy. This problem is one of 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