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ON THE ACCURACY OF WEAK-LENSING CLUSTER MASS RECONSTRUCTIONS

M. R. BeckerDepartment of Physics, 5720 S. Ellis Avenue, The University of Chicago, Chicago, IL 60637, USAAndrey V. KravtsovDepartment of Astronomy and Astrophysics, 5640 South Ellis Avenue, The University of Chicago, Chicago, IL 60637, USA
2011en
ABI

Аннотация

We study the bias and scatter in mass measurements of galaxy clusters resulting from fitting a spherically-symmetric Navarro, Frenk & White model to the reduced tangential shear profile measured in weak lensing observations. The reduced shear profiles are generated for ~10^4 cluster-sized halos formed in a LCDM cosmological N-body simulation of a 1 Gpc/h box. In agreement with previous studies, we find that the scatter in the weak lensing masses derived using this fitting method has irreducible contributions from the triaxial shapes of cluster-sized halos and uncorrelated large-scale matter projections along the line-of-sight. Additionally, we find that correlated large-scale structure within several virial radii of clusters contributes a smaller, but nevertheless significant, amount to the scatter. The intrinsic scatter due to these physical sources is ~20% for massive clusters, and can be as high as ~30% for group-sized systems. For current, ground-based observations, however, the total scatter should be dominated by shape noise from the background galaxies used to measure the shear. Importantly, we find that weak lensing mass measurements can have a small, ~5%-10%, but non-negligible amount of bias. Given that weak lensing measurements of cluster masses are a powerful way to calibrate cluster mass-observable relations for precision cosmological constraints, we strongly emphasize that a robust calibration of the bias requires detailed simulations which include more observational effects than we consider here. Such a calibration exercise needs to be carried out for each specific weak lensing mass estimation method, as the details of the method determine in part the expected scatter and bias. We present an iterative method for estimating mass M500c that can eliminate the bias for analyses of ground-based data.

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