Open AccessModel selection as a science driver for dark energy surveys
Author(s) -
Mukherjee Pia,
Parkinson David,
Corasaniti Pier Stefano,
Liddle Andrew R.,
Kunz Martin
Publication year - 2006
Publication title -
monthly notices of the royal astronomical society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.058
H-Index - 383
eISSN - 1365-2966
pISSN - 0035-8711
DOI - 10.1111/j.1365-2966.2006.10427.x
Subject(s) - dark energy , bayes factor , physics , bayes' theorem , selection (genetic algorithm) , model selection , astrophysics , supernova , bayesian probability , cosmology , astronomy , machine learning , artificial intelligence , computer science
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.