Weak lensing peak count as a probe of f(R) theories

Abstract

Weak gravitational lensing by galaxy clusters on faint higher redshift galaxies has been traditionally used to study the cluster mass distribution and as a tool to identify clusters as peaks in the shear maps. However, it becomes soon clear that peaks statistics can also be used as a way to constrain the underlying cosmological model due to its dependence on both the cosmic expansion rate and the growth rate of structures. This feature makes peak statistics particularly interesting from the point of view of discriminating between General Relativity and modified gravity. Here we consider a general class of $f(R)$ theories and compute the observable mass function based on the aperture mass statistics. We complement our theoretical analysis with a Fisher matrix forecast of the constraints that an Euclid\,-\,like survey can impose on the $f(R)$ model parameters. We show that peak statistics alone can in principle discriminate between General Relativity and $f(R)$ models and strongly constrain the $f(R)$ parameters that are sensitive to the non-linear growth of structure. However, further analysis is needed in order to include possible selection function in the peaks redshift determination.

17 pages, 9 figures, 1 table, accepted for publication on MNRAS on Jan 14, 2013; updated to match the published version

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• Bayesian forward modelling of cosmic shear data
• Chameleons in the Early Universe: Kicks, Rebounds, and Particle Production
• Cosmological test of gravity using weak lensing voids
• Reconciling galaxy cluster shapes, measured by theorists vs observers
• Extreme value statistics of weak lensing shear peak counts
• The self similarity of weak lensing peaks
• The Correspondence between Convergence Peaks from Weak Lensing and Massive Dark Matter Haloes
• Optimal void finders in weak lensing maps
• Constraining f(R) theories with cosmography
• Constraining the mass-concentration relation through weak lensing peak function
• Hubble drift in Palatini $f(\mathcal{R})$-theories
• Detectable Data-driven Features in the Primordial Scalar Power Spectrum
• Mass - concentration relation and weak lensing peak counts
• MADLens, a python package for fast and differentiable non-Gaussian lensing simulations