University of Sussex
Browse
MNRAS-2006-Mukherjee-1725-34.pdf (302.68 kB)

Model selection as a science driver for dark energy surveys

Download (302.68 kB)
journal contribution
posted on 2023-06-08, 09:52 authored by Pia Mukherjee, David Parkinson, Pier Stefano Corasaniti, Andrew R Liddle, Martin Kunz
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.

History

Publication status

  • Published

File Version

  • Published version

Journal

Monthly Notices of the Royal Astronomical Society

ISSN

0035-8711

Publisher

Wiley-Blackwell

Issue

4

Volume

369

Page range

1725-1734

Department affiliated with

  • Physics and Astronomy Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2012-02-06

First Open Access (FOA) Date

2016-03-22

First Compliant Deposit (FCD) Date

2016-11-16

Usage metrics

    University of Sussex (Publications)

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC