2007
DOI: 10.1111/j.1365-2966.2007.12134.x
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On model selection forecasting, dark energy and modified gravity

Abstract: 13.02.13 KB. Accepted version ok to add to Spiral. RAS/Wile

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Cited by 164 publications
(212 citation statements)
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“…It is therefore more useful to combine lensing with other observations of large-scale structure to carry out robust tests of gravity, as shown in Sections 10.2.5 and 10.3.1 below. Tests of gravity that rely solely on lensing measurements can constrain specific models; alternatively lensing measurements with multiple redshift bins can probe the the growth of structure (e.g., [960]) which can be compared to the predictions of GR.…”
Section: Weak Gravitational Lensingmentioning
confidence: 99%
“…It is therefore more useful to combine lensing with other observations of large-scale structure to carry out robust tests of gravity, as shown in Sections 10.2.5 and 10.3.1 below. Tests of gravity that rely solely on lensing measurements can constrain specific models; alternatively lensing measurements with multiple redshift bins can probe the the growth of structure (e.g., [960]) which can be compared to the predictions of GR.…”
Section: Weak Gravitational Lensingmentioning
confidence: 99%
“…The value of the CMB prior is shown in terms of f (φ) in the first column of Table 3 for each of the parameters. Since the LS is a future survey we also compared its results with a prediction of the Planck Fisher matrix (see Heavens et al 2007). The predicted Fisher matrix in Heavens et al (2007) assumes zero curvature (Ω m + Ω Λ = 1), therefore, we had to impose this constraint on our COSEBIs Fisher Matrices to add them.…”
Section: Assumptions and Parameter Settingsmentioning
confidence: 99%
“…Then, if the correct underlying model does contain certain systematics, in the incorrect model where we neglect them, the maximum of the expected likelihood will not, in general, be at the correct parameter values (see Heavens et al 2007, Fig. 1).…”
Section: Appendix A: Bias On Parameter Estimationmentioning
confidence: 99%
“…Heavens et al 2007;Camera et al 2011c). Two models are nested when the parameter space of the former (or 'simpler' model) is contained within that of the latter.…”
Section: Appendix A: Bias On Parameter Estimationmentioning
confidence: 99%
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