Statistical lensing by clusters of galaxies and Dark Matter distribution

Abstract

We present a dynamical study of clusters of galaxies based on a statistical analysis of the distribution of arclets in cluster-lenses. Arclets, identified as small ellipses, and their spatial distribution are used to recover the potential of the cluster. We tested the efficiency of the method by using a simulated CCD field with random distributions of circular sources at redshift above 1 and an analytic spherical potential well. Although arclets can give a lot of information about the cluster mass distribution, the crowding by cluster galaxies preclude to infer anything in the very central region. We then complemented the study by a theoretical model of clusters to infer the Dark Matter distribution. We assume that galaxies represent only a small part of the total mass and do not assume that mass traces light. Thus we solve the coupled problem of galaxies interacting gravitationally in a large well of Dark Matter. In the case of spherical clusters with arcs, we are able to recover the cluster parameters for both the Dark Matter and the Visible Matter (galaxies distribution). We are presently extending the method to more realistic lenses and source distribution, and we will use it further on real ultra-deep CCD frames.