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Constraining cosmology with shear peak statistics: tomographic analysis

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, Ωm, 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.
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Nicolas Martinet, James G. Bartlett, Alina Kiessling, Barbara Sartoris. Constraining cosmology with shear peak statistics: tomographic analysis. Astronomy and Astrophysics - A&A, EDP Sciences, 2015, 581, pp.A101. ⟨10.1051/0004-6361/201425164⟩. ⟨cea-01383673⟩



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