Model selection as a science driver for dark energy surveys

Mukherjee, Pia, Parkinson, David, Corasaniti, Pier S., Liddle, Andrew R. and Kunz, Martin (2006) Model selection as a science driver for dark energy surveys. Monthly Notices of the Royal Astronomical Society, 369 4: 1725-1734. doi:10.1111/j.1365-2966.2006.10427.x

Author Mukherjee, Pia
Parkinson, David
Corasaniti, Pier S.
Liddle, Andrew R.
Kunz, Martin
Title Model selection as a science driver for dark energy surveys
Journal name Monthly Notices of the Royal Astronomical Society   Check publisher's open access policy
ISSN 0035-8711
Publication date 2006-07
Sub-type Article (original research)
DOI 10.1111/j.1365-2966.2006.10427.x
Open Access Status DOI
Volume 369
Issue 4
Start page 1725
End page 1734
Total pages 10
Place of publication Oxford, United Kingdom
Publisher Oxford University Press
Language eng
Abstract 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.
Keyword methods : statistical
cosmological parameters
cosmology : theory
cosmological parameters
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Mathematics and Physics
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Citation counts: TR Web of Science Citation Count  Cited 42 times in Thomson Reuters Web of Science Article | Citations
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Created: Mon, 28 Feb 2011, 09:46:11 EST