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