Cosmological tests of general relativity: A principal component analysis

The next generation of weak lensing surveys will trace the evolution of matter perturbations and gravitational potentials from the matter dominated epoch until today. Along with constraining the dynamics of dark energy, they will probe the relations between matter overdensities, local curvature, and...

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Bibliographic Details
Main Authors: Hojjati, Alireza, Zhao, Gong-Bo, Pogosian, Levon, Silvestri, Alessandra, Crittenden, Robert, Koyama, Kazuya
Other Authors: Massachusetts Institute of Technology. Department of Physics
Format: Article
Language:en_US
Published: American Physical Society 2012
Online Access:http://hdl.handle.net/1721.1/71902
Description
Summary:The next generation of weak lensing surveys will trace the evolution of matter perturbations and gravitational potentials from the matter dominated epoch until today. Along with constraining the dynamics of dark energy, they will probe the relations between matter overdensities, local curvature, and the Newtonian potential. We work with two functions of time and scale to account for any modifications of these relations in the linear regime from those in the ΛCDM model. We perform a principal component analysis (PCA) to find the eigenmodes and eigenvalues of these functions for surveys like the Dark Energy Survey and Large Synoptic Survey Telescope. This paper builds on and significantly extends the PCA analysis of Zhao et al. [ Phys. Rev. Lett. 103 241301 (2009)] in several ways. In particular, we consider the impact of some of the systematic effects expected in weak lensing surveys. We also present the PCA in terms of other choices of the two functions needed to parametrize modified growth on linear scales, and discuss their merits. We analyze the degeneracy between the modified growth functions and other cosmological parameters, paying special attention to the effective equation of state w(z). Finally, we demonstrate the utility of the PCA as an efficient data compression stage which enables one to easily derive constraints on parameters of specific models without recalculating Fisher matrices from scratch.