Weak-lensing mass bias in merging galaxy clusters
Wonki Lee, Sangjun Cha, M. James Jee, Daisuke Nagai, Lindsay King,, John ZuHone, Urmila Chadayammuri, Sharon Felix, Kyle Finner

TL;DR
This study uses hydrodynamical simulations to quantify weak lensing mass bias in merging galaxy clusters, revealing significant overestimations in mass during mergers, especially near the first pericenter passage.
Contribution
It provides the first detailed analysis of WL mass bias evolution in binary mergers using realistic simulations and applies findings to real observed clusters.
Findings
Mass bias can reach up to 60% in massive mergers.
Bias depends on merger stage and mass ratio.
Previous WL studies may overestimate cluster masses in mergers.
Abstract
Although weak lensing (WL) is a powerful method to estimate a galaxy cluster mass without any dynamical assumptions, a model bias can arise when the cluster density profile departs from the assumed model profile. In a merging system, the bias is expected to become most severe because the constituent halos undergo significant structural changes. In this study, we investigate WL mass bias in binary cluster mergers using a suite of idealized hydrodynamical simulations. Realistic WL shear catalogs are generated by matching the source galaxy properties, such as intrinsic shape dispersion, measurement noise, source densities, etc., to those from Subaru and {\it Hubble Space Telescope} observations. We find that, with the typical mass-concentration (-) relation and the Navarro-Frenk-White (NFW) profile, the halo mass bias depends on the time since the first pericenter passage and…
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Taxonomy
TopicsAdaptive optics and wavefront sensing · Astronomy and Astrophysical Research · Galaxies: Formation, Evolution, Phenomena
