Multiple-Images in the Cluster Lens Abell 2218: Constraining the Geometry of the Universe ?

In this Letter we present a detailed study of the lensing configuration in the cluster Abell 2218. Four multiple-images systems with measured spectroscopic redshifts have been identified in this cluster. These multiple images are very useful to constrain accurately the mass distribution in the cluster core, but they are also sensitive to the value of the geometrical cosmological parameters of the Universe. Using a simplified maximum likelihood analysis we find 0 < Omega_M < 0.30 assuming a flat Universe, and 0 < Omega_M < 0.33 and w < -0.85 for a flat Universe with dark energy. Interestingly, an Einstein-de Sitter model is excluded at more than 4sigma. These constraints are consistent with the current constraints derived with CMB anisotropies or supernovae studies. The proposed method constitutes an independent test of the geometrical cosmological parameters of the Universe and we discuss the limits of this method and this particular application to Abell 2218. Application of this method with more sophisticated tools and to a larger number of clusters or with more multiple images constraints, will put stringent constraints on the geometrical cosmological parameters.Comment: 5 pages, 3 figures. Accepted for publication by Astronomy & Astrophysic

Ringing effects reduction by improved deconvolution algorithm Application to A370 CFHT image of gravitational arcs

We develop a self-consistent automatic procedure to restore informations from astronomical observations. It relies on both a new deconvolution algorithm called LBCA (Lower Bound Constraint Algorithm) and the use of the Wiener filter. In order to explore its scientific potential for strong and weak gravitational lensing, we process a CFHT image of the galaxies cluster Abell 370 which exhibits spectacular strong gravitational lensing effects. A high quality restoration is here of particular interest to map the dark matter within the cluster. We show that the LBCA turns out specially efficient to reduce ringing effects introduced by classical deconvolution algorithms in images with a high background. The method allows us to make a blind detection of the radial arc and to recover morphological properties similar to thoseobserved from HST data. We also show that the Wiener filter is suitable to stop the iterative process before noise amplification, using only the unrestored data.Comment: A&A in press 9 pages 9 figure