Reconstruction of Cluster Masses using Particle Based Lensing I: Application to Weak Lensing

TL;DR

Particle-Based Lensing (PBL) is a novel method for galaxy cluster mass reconstruction that treats each lensed galaxy as a particle, allowing for adaptive, high-resolution mapping of mass distributions using all lensing observables.

Contribution

The paper introduces PBL, a new particle-based approach for gravitational lensing mass reconstruction that improves detection of substructures and cusps over traditional grid-based methods.

Findings

PBL outperforms grid-based methods in identifying substructures.

PBL successfully detects substructure in the Bullet Cluster without strong lensing data.

The method provides constant signal-to-noise and exploits high information density regions.

Abstract

We present Particle-Based Lensing (PBL), a new technique for gravitational lensing mass reconstructions of galaxy clusters. Traditionally, most methods have employed either a finite inversion or gridding to turn observational lensed galaxy ellipticities into an estimate of the surface mass density of a galaxy cluster. We approach the problem from a different perspective, motivated by the success of multi-scale analysis in smoothed particle hydrodynamics. In PBL, we treat each of the lensed galaxies as a particle and then reconstruct the potential by smoothing over a local kernel with variable smoothing scale. In this way, we can tune a reconstruction to produce constant signal-noise throughout, and maximally exploit regions of high information density. PBL is designed to include all lensing observables, including multiple image positions and fluxes from strong lensing, as well as weak lensing signals including shear and flexion. In this paper, however, we describe a shear-only reconstruction, and apply the method to several test cases, including simulated lensing clusters, as well as the well-studied ``Bullet Cluster'' (1E0657-56). In the former cases, we show that PBL is better able to identify cusps and substructures than are grid-based reconstructions, and in the latter case, we show that PBL is able to identify substructure in the Bullet Cluster without even exploiting strong lensing measurements. We also make our codes publicly available.