Nonlinear Weak Lensing reconstruction for Galaxy Clusters

TL;DR

This paper introduces a new nonlinear weak lensing mass reconstruction method for galaxy clusters that improves accuracy in dense core regions by using masking and model-based initial guesses.

Contribution

The authors develop a novel reconstruction framework that overcomes instability issues of traditional methods in cluster cores, validated with simulated data.

Findings

High-fidelity mass reconstruction achieved in unmasked regions

Residuals below 0.02 sigma in ideal conditions

Method extends accurate reconstruction into nonlinear regime

Abstract

We present a numerical investigation of nonlinear cluster lens reconstruction using weak lensing mass mapping. Recent advances in imaging and shear estimation have pushed reliable reduced shear measurements closer to cluster cores, making mass reconstruction accessible in the nonlinear regime. However, the Kaiser-Squires based algorithm becomes unstable in cluster cores, where convergence $\kappa$ significantly deviates from zero and the linear approximation breaks down. To address this limitation, we develop a reconstruction framework with two key modifications: applying smooth masks to these regions and using a model-derived analytical solution as the initial guess, rather than assuming $\kappa = 0$. We validate our framework using simulated cluster lensing data with known mass distributions, incorporating realistic masks that arise from limitations in reduced shear measurements. We show that in the absence of shape noise, our framework yields high-fidelity mass reconstruction in regions of large reduced shear, with the best-performing method achieving residuals below $0.02 \sigma$ in the unmasked regions. This pushes mass reconstruction to higher accuracy in the nonlinear regime.