On model selection forecasting, Dark Energy and modified gravity

By Alan Heavens, Thomas D. Kitching, L. Verde. 2007

147 citations recorded by the source corpus.

ABSTRACT The Fisher matrix approach (Fisher (1935)) allows one to calculate in advance howwell a given experiment will be able to estimate model parameters, and has been aninvaluable tool in experimental design. In the same spirit, we present here a methodto predict how well a given experiment can distinguish between diﬀerent models, re-gardless of their parameters. From a Bayesian viewpoint, this involves computation ofthe Bayesian evidence. In this paper, we generalise the Fisher matrix approach fromthe context of parameter ﬁtting to that of model testing, and show how the expectedevidence can be computed under the same simplifying assumption of a gaussian like-lihood as the Fisher matrix approach for parameter estimation. With this ‘Laplaceapproximation’ all that is needed to compute the expected evidence is the Fisher ma-trix itself. We illustrate the method with a study of how well upcoming and plannedexperiments should perform at distinguishing between Dark Energy models and mod-iﬁed gravity theories. In particular we consider the combination of 3D weak lensing,for which planned and proposed wide-ﬁeld multi-band imaging surveys will providesuitable data, and probes of the expansion history of the Universe, such as proposedsupernova and baryonic acoustic oscillations surveys. We ﬁnd that proposed large-scale weak lensing surveys from space should be able readily to distinguish GeneralRelativity from modiﬁed gravity models.Key words: methods: statistical, cosmological parameters, dark energy

Subfields: Astronomy and Astrophysics

View Paper: https://www.semanticscholar.org/paper/0189cbefc03465055a965aa259c64e8f9444550b

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In Astronomy and Astrophysics

Citations in the corpus

Beyond the Cosmological Standard Model (2014, unknown): “, [960]) which can be compared to the predictions of GR.”
Cosmology and Fundamental Physics with the Euclid Satellite (2012, methodology): “The WLS will be able to rule out the DGP model growth index with a Bayes factor | lnB| ' 50 [579], and viable phenomenological extensions could be detected at the 3σ level for 1000 .”