Prospective Cosmological Constraints From Combining Stacked-Cluster Weak Lensing With Cluster-Galaxy Cross-Correlation and Galaxy Auto-Correlations

Andres Salcedo, Ohio State University

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The observed distribution of galaxies and galaxy clusters traces the underlying cosmic large-scale structure. This structure evolved gravitationally from initial fluctuations in the primordial matter-density field. As a result, large-scale structure is particularly sensitive to the cosmological properties of the primordial matter-density field. This sensitivity can be used to constrain cosmology through the combination of data from galaxy redshift surveys such as the Dark Energy Survey (DES) and dark-matter-only N-body simulations. Using a grid of cosmological N-body simulations, we measure derivatives of the cluster-galaxy and galaxy-galaxy projected cross correlation functions as well as the cluster weak lensing signal as a function of cosmological, halo occupation distribution (HOD) and cluster mass-observable parameters. We show that combining information from these probes across a large range of scales (0.3 to 30.0 Mpc/h) can help break degeneracies between cosmological and HOD/cluster mass-observable parameters that would otherwise degrade constraints from clustering or lensing alone. We present forecasts for a fiducial case corresponding to DES clusters and galaxies in a redshift bin from z=0.35 to z=0.55, which yields percent-level constraints on the amplitude of matter clustering. We further investigate the contribution to this constraint from different combinations of our observables at large and small scales and find that all scales of each observable contribute significantly to the tightness of the constraint. Given accurate modeling, percent-level constraints on the amplitude of matter clustering are achievable with currently available datasets such as those from the Dark Energy Survey.

Abstract Author(s): Andres N. Salcedo, Benjamin D. Wibking, David H. Weinberg, Hao-Yi Wu, Lehman Garrison, Douglas Ferrer, Jeremy Tinker, Daniel Eisenstein, Phillip Pinto