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The detection of galaxy clusters in present and future surveys enablesmeasuring mass-to-light ratios, clustering properties or galaxy clusterabundances and therefore, constraining cosmological parameters. We present anew technique for detecting galaxy clusters, which is based on the MatchedFilter Algorithm from a Bayesian point of view. The method is able to determinethe position, redshift and richness of the cluster through the maximization ofa filter depending on galaxy luminosity, density and photometric redshiftcombined with a galaxy cluster prior. We tested the algorithm through realisticmock galaxy catalogs, revealing that the detections are 100% complete and 80%pure for clusters up to z <1.2 and richer than \Lambda > 25 (Abell Richness >0). We applied the algorithm to the CFHTLS Archive Research Survey (CARS) data,recovering similar detections as previously published using the same data plusadditional clusters that are very probably real. We also applied this algorithmto the Deep Lens Survey (DLS), obtaining the first sample of optical-selectedgalaxy in this survey. The sample is complete up to redshift 0.7 and we detectmore than 780 cluster candidates up to redshift 1.2. We conclude by discussingthe differences between previous weak lensing detections in this survey andoptical detections in both samples.

Detecting Galaxy Clusters in the DLS and CARS: a Bayesian Cluster Finder