Constraining cosmology with shear peak statistics: tomographic analysis

¹ UPMC Université Paris 06, UMR 7095, Institut d’Astrophysique de Paris, 98bis Bd Arago, 75014 Paris, France
e-mail: martinet@iap.fr
² APC, AstroParticule et Cosmologie, Université Paris Diderot, CNRS/IN2P3, CEA/lrfu, Observatoire de Paris, Sorbonne Paris Cité, 10 rue Alice Domon et Léonie Duquet, 75205 Paris Cedex 13, France
³ Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, California, USA
⁴ Dipartimento di Fisica, Sezione di Astronomia, Università di Trieste, via Tiepolo 11, 34143 Trieste, Italy
⁵ INAF/Osservatorio Astronomico di Trieste, via Tiepolo 11, 34143 Trieste, Italy

Received: 16 October 2014
Accepted: 8 April 2015

Abstract

The abundance of peaks in weak gravitational lensing maps is a potentially powerful cosmological tool, complementary to measurements of the shear power spectrum. We study peaks detected directly in shear maps, rather than convergence maps, an approach that has the advantage of working directly with the observable quantity, the galaxy ellipticity catalog. Using large numbers of numerical simulations to accurately predict the abundance of peaks and their covariance, we quantify the cosmological constraints attainable by a large-area survey similar to that expected from the Euclid mission, focusing on the density parameter, Ω_m, and on the power spectrum normalization, σ₈, for illustration. We present a tomographic peak counting method that improves the conditional (marginal) constraints by a factor of 1.2 (2) over those from a two-dimensional (i.e., non-tomographic) peak-count analysis. We find that peak statistics provide constraints an order of magnitude less accurate than those from the cluster sample in the ideal situation of a perfectly known observable-mass relation; however, when the scaling relation is not known a priori, the shear-peak constraints are twice as strong and orthogonal to the cluster constraints, highlighting the value of using both clusters and shear-peak statistics.