Detecting Galaxy Clusters in the DLS and CARS: a Bayesian Cluster Finder
  The detection of galaxy clusters in present and future surveys enables
measuring mass-to-light ratios, clustering properties or galaxy cluster
abundances and therefore, constraining cosmological parameters. We present a
new technique for detecting galaxy clusters, which is based on the Matched
Filter Algorithm from a Bayesian point of view. The method is able to determine
the position, redshift and richness of the cluster through the maximization of
a filter depending on galaxy luminosity, density and photometric redshift
combined with a galaxy cluster prior. We tested the algorithm through realistic
mock 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 plus
additional clusters that are very probably real. We also applied this algorithm
to the Deep Lens Survey (DLS), obtaining the first sample of optical-selected
galaxy in this survey. The sample is complete up to redshift 0.7 and we detect
more than 780 cluster candidates up to redshift 1.2. We conclude by discussing
the differences between previous weak lensing detections in this survey and
optical detections in both samples.
