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arxiv: 1502.05872 · v1 · pith:JX46INZ5new · submitted 2015-02-20 · 🌌 astro-ph.CO

Weak lensing reconstructions in 2D & 3D: implications for cluster studies

classification 🌌 astro-ph.CO
keywords reconstructionsclusterclustersdetectionlensingmassweakglimpse
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We compare the efficiency with which 2D and 3D weak lensing mass mapping techniques are able to detect clusters of galaxies using two state-of-the-art mass reconstruction techniques: MRLens in 2D and GLIMPSE in 3D. We simulate otherwise-empty cluster fields for 96 different virial mass-redshift combinations spanning the ranges $3\times10^{13}h^{-1}M_\odot \le M_{vir}\le 10^{15}h^{-1}M_\odot$ and $0.05 \le z_{\rm cl} \le 0.75$, and for each generate 1000 realisations of noisy shear data in 2D and 3D. For each field, we then compute the cluster (false) detection rate as the mean number of cluster (false) detections per reconstruction over the sample of 1000 reconstructions. We show that both MRLens and GLIMPSE are effective tools for the detection of clusters from weak lensing measurements, and provide comparable quality reconstructions at low redshift. At high redshift, GLIMPSE reconstructions offer increased sensitivity in the detection of clusters, yielding cluster detection rates up to a factor of $\sim 10\times$ that seen in 2D reconstructions using MRLens. We conclude that 3D mass mapping techniques are more efficient for the detection of clusters of galaxies in weak lensing surveys than 2D methods, particularly since 3D reconstructions yield unbiased estimators of both the mass and redshift of the detected clusters directly.

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  1. Machine-learning applications for weak-lensing cosmology

    astro-ph.CO 2026-05 unverdicted novelty 2.0

    Machine learning techniques can mitigate limitations in traditional weak-lensing analyses and enhance extraction of cosmological information from galaxy imaging surveys.