We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.

Durham Research Online
You are in:

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

Tam, S.-I. and Massey, R. and Jauzac, M. and Robertson, A. (2020) 'Mapping dark matter and finding filaments : calibration of lensing analysis techniques on simulated data.', Monthly notices of the Royal Astronomical Society., 496 (3). pp. 3973-3990.


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.

Item Type:Article
Full text:(VoR) Version of Record
Download PDF
Publisher Web site:
Publisher statement:This article has been accepted for publication in Monthly notices of the Royal Astronomical Society. ©: 2020 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.
Date accepted:12 June 2020
Date deposited:21 October 2020
Date of first online publication:19 June 2020
Date first made open access:21 October 2020

Save or Share this output

Look up in GoogleScholar