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Strong & Weak Lensing United: the Cluster Mass Distribution of RX J1347–1145

Published online by Cambridge University Press:  15 June 2005

M. Bradač
Affiliation:
IAEF, University of Bonn, Auf dem Hügel 71, D-53121 Bonn, Germany Max-Planck-Institut für Radioastronomie, Auf dem Hügel 69, D-53121 Bonn, Germany
P. Schneider
Affiliation:
IAEF, University of Bonn, Auf dem Hügel 71, D-53121 Bonn, Germany
T. Erben
Affiliation:
IAEF, University of Bonn, Auf dem Hügel 71, D-53121 Bonn, Germany
M. Lombardi
Affiliation:
IAEF, University of Bonn, Auf dem Hügel 71, D-53121 Bonn, Germany ESO, Karl-Schwarzschild-Str. 2, D-85748 Garching bei München, Germany
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Abstract

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Weak gravitational lensing is considered to be one of the most powerful tools to study the mass and the mass distribution of galaxy clusters. However, the mass-sheet degeneracy transformation has limited its success. We present a novel method for a cluster mass reconstruction, which combines weak and strong lensing information on common scales and can as a consequence break the mass-sheet degeneracy. We extend the weak lensing formalism to the inner parts of the cluster, use redshift information of background sources and combine these with the constraints from multiple image systems. We apply the method to N-body simulations as well as to strong and weak lensing ground-based multi-colour data of RX J1347–1145, the most X-ray luminous cluster known to date. If the redshift measurements of background sources (for strong and weak lensing) and the identification of the multiple-image system are correct, we estimate the enclosed cluster mass within $360\: {\rm h}^{-1}\mbox{kpc}$ to $M(<360\: {\rm h}^{-1}\mbox{kpc})= (1.2 \pm 0.3) \times 10^{15} M_{\odot}$. With higher resolution (e.g. HST) imaging data, reliable multiple imaging information could be obtained and the reconstruction further improved.To search for other articles by the author(s) go to: http://adsabs.harvard.edu/abstract_service.html

Type
Contributed Papers
Copyright
© 2004 International Astronomical Union