Weak lensing mass modeling bias and the impact of miscentring

Parametric modeling of galaxy cluster density profiles from weak lensing observations leads to a mass bias, whose detailed understanding is critical in deriving accurate mass-observable relations for constraining cosmological models. Drawing from existing methods, we develop a robust framework for calculating this mass bias in one-parameter fits to simulations of dark matter haloes. We show that our approach has the advantage of being independent of the absolute noise level, so that only the number of haloes in a given simulation and the representativeness of the simulated haloes for real clusters limit the accuracy of the bias estimation. While we model the bias as a lognormal distribution and the haloes with a Navarro-Frenk-White profile, our method can be generalized to any bias distribution and parametric model of the radial mass distribution. We find that the lognormal assumption is not strictly valid in the presence of miscentring of haloes. We investigate the use of cluster centres derived from weak lensing in the context of mass bias, and tentatively find that such centroids can yield sensible mass estimates if the convergence peak has a signal-to-noise ratio (SNR) approximately greater than 4. In this context we also find that the standard approach to estimating the positional uncertainty of weak lensing mass peaks using bootstrapping severely underestimates the true positional uncertainty for peaks with low SNRs. Though we determine the mass and redshift dependence of the bias distribution for a few experimental setups, our focus remains providing a general approach to computing such distributions.

Automated summary

Parametric modeling of galaxy cluster density profiles from weak lensing observations can lead to a mass bias, which is important for accurate mass-observable relations in cosmological models. A robust framework for calculating this mass bias in one-parameter fits to simulations of dark matter haloes has been developed. The approach is independent of noise level and the accuracy of bias estimation depends on the number and representativeness of haloes in simulations.