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

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 overestimation 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.