GaBoDS: the Garching-Bonn Deep Survey^*,**

IX. A sample of 158 shear-selected mass concentration candidates

¹ Isaac Newton Group of Telescopes, Calle Alvarez Abreu 70, 38700 Santa Cruz de La Palma, Spain e-mail: mischa@ing.iac.es
² Argelander-Institut für Astronomie (AIfA), Universität Bonn, Auf dem Hügel 71, 53121 Bonn, Germany

Received: 3 July 2006
Accepted: 6 November 2006

Abstract

Aims.The aim of the present work is the construction of a mass-selected galaxy cluster sample based on weak gravitational lensing methods. This sample will be subject to spectroscopic follow-up observations.

Methods.We apply the mass aperture statistics (S-statistics) and a new derivative of it (the P-statistics) to 19 square degrees of high quality, single colour wide field imaging data obtained with the WFI@MPG/ESO 2.2 m telescope. For the statistics a family of filter functions is used that approximates the expected tangential radial shear profile and thus allows for the efficient detection of mass concentrations. The exact performance of the P-statistics still needs to be evaluated by means of simulations.

Results.We find that the two samples of mass concentrations found with the P- and S-statistics have very similar properties. The overlap between them increases with the of the detections made. In total, we present a combined list of 158 possible mass concentrations, which is the first time that such a large and blindly selected sample is published. 72 of the detections are associated with concentrations of bright galaxies. For about 22 of those we found spectra in the literature, indicating or proving that the galaxies seen are indeed spatially concentrated. 16 of those were previously known to be clusters or have meanwhile been secured as such. We currently follow-up a larger number of them spectroscopically to obtain deeper insight into their physical properties. The remaining 55% of the possible mass concentrations found are not associated with any optical light. We show that those “dark” detections are mostly due to noise, and appear preferentially in shallow data.