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Cluster cross‐sections for strong lensing: analytic and numerical lens models
Author(s) -
Meneghetti Massimo,
Bartelmann Matthias,
Moscardini Lauro
Publication year - 2003
Publication title -
monthly notices of the royal astronomical society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.058
H-Index - 383
eISSN - 1365-2966
pISSN - 0035-8711
DOI - 10.1046/j.1365-8711.2003.06276.x
Subject(s) - physics , multipole expansion , substructure , cosmology , cluster (spacecraft) , analytic function , gravitational lens , lens (geology) , virial mass , statistical physics , theoretical physics , astrophysics , classical mechanics , galaxy cluster , redshift , mathematical analysis , galaxy , optics , quantum mechanics , mathematics , structural engineering , computer science , engineering , programming language
The statistics of gravitationally lensed arcs was recognized earlier as a potentially powerful cosmological probe. However, while fully numerical models find orders of magnitude difference between the arc probabilities in different cosmological models, analytic models tend to find markedly different results. In this paper we introduce an analytic cluster lens model that improves upon existing analytic models in four ways. (i) We use the more realistic Navarro–Frenk–White profile instead of singular isothermal spheres, (ii) we include the effect of cosmology on the compactness of the lenses, (iii) we use elliptical instead of axially symmetric lenses and (iv) we take the intrinsic ellipticity of sources into account. While these improvements to the analytic model lead to a pronounced increase of the arc probability, comparisons with numerical models of the same virial mass demonstrate that the analytic models still fall short by a substantial margin of reproducing the results obtained with numerical models. Using multipole expansions of cluster mass distributions, we show that the remaining discrepancy can be attributed to substructure inside clusters and tidal fields contributed by the cluster surroundings, effects that cannot reasonably and reliably be mimicked in analytic models.

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