Galaxy cluster detection with optical and infrared imaging

Being galaxy clusters the most massive bound structures in the Universe, they represent a powerful tool to probe the large-scale structure predicted by the standard cosmological model, and to understand how environmental effects affect galaxy evolution. To conduct these studies and obtain reliable results, it is important to build complete and pure cluster catalogs. The use of these catalogs for cosmology requires accurate estimates of cluster mass. In this work, I describe the cluster detection algorithm that I developed during my PhD thesis : Red-GOLD, and the results that I obtained by applying i to current multi-wavelength surveys. My algorithm is based on the detection of galaxy overdensities and the characterisation of their red-sequence. The algorithm finds red galaxy overdensities with respect to the mean background. I select red galaxies using color predictions given by stellar population synthesis models and impose color limits as a function of redshift. Among those galaxies, I discern the early-type galaxies from their spectral type. I then identify cluster members using accurate photometric redshifts, and estimate the cluster candidate richness. I applied Red-GOLD to optical data coming from two different surveys, the Next Generation Virgo Cluster Survey (NGVS) and the Canada-France-Hawaii Telescope Lensing Survey (CFHTLS) and detected galaxy cluster candidates up to redshift z=1. I assessed the performances of my algorithm by applying it to simulated galaxy catalogs from the Millennium simulations. My cluster catalogue is complete at the 80% up to redshift z=1 and pure at 81%.