Mapping dark matter and finding filaments: calibration of lensing analysis techniques on simulated data

TL;DR

This paper evaluates and compares different mass mapping techniques for gravitational lensing data of galaxy clusters, highlighting their biases, noise characteristics, and potential for filament detection in upcoming high-resolution surveys.

Contribution

It provides a comprehensive assessment of lensing analysis methods on simulated data, identifying optimal approaches for various scientific goals and the prospects for filament detection.

Findings

Mass maps via direct shear inversion are unbiased.

Filtering reduces noise but may introduce bias.

High-density space-based surveys can detect filaments around clusters.

Abstract

We quantify the performance of mass mapping techniques on mock imaging and gravitational lensing data of galaxy clusters. The optimum method depends upon the scientific goal. We assess measurements of clusters' radial density profiles, departures from sphericity, and their filamentary attachment to the cosmic web. We find that mass maps produced by direct (KS93) inversion of shear measurements are unbiased, and that their noise can be suppressed via filtering with MRLens. Forward-fitting techniques, such as Lenstool, suppress noise further, but at a cost of biased ellipticity in the cluster core and over-estimation of mass at large radii. Interestingly, current searches for filaments are noise-limited by the intrinsic shapes of weakly lensed galaxies, rather than by the projection of line-of-sight structures. Therefore, space-based or balloon-based imaging surveys that resolve a high density of lensed galaxies, could soon detect one or two filaments around most clusters.