MNRAS-2006-Mukherjee-1725-34.pdf (302.68 kB)
Model selection as a science driver for dark energy surveys
journal contribution
posted on 2023-06-08, 09:52 authored by Pia Mukherjee, David Parkinson, Pier Stefano Corasaniti, Andrew R Liddle, Martin KunzA 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.
History
Publication status
- Published
File Version
- Published version
Journal
Monthly Notices of the Royal Astronomical SocietyISSN
0035-8711Publisher
Wiley-BlackwellExternal DOI
Issue
4Volume
369Page range
1725-1734Department affiliated with
- Physics and Astronomy Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2012-02-06First Open Access (FOA) Date
2016-03-22First Compliant Deposit (FCD) Date
2016-11-16Usage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC