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We present the methods and first results of the search for galaxy clusters in the Kilo Degree Survey (KiDS). The adopted algorithm and the criterium for selecting the member galaxies are illustrated. Here we report the preliminary results obtained over a small area (7 sq. degrees), and the comparison of our cluster candidates with those found in the RedMapper and SZ Planck catalogues; the analysis to a larger area (148 sq. degrees) is currently in progress. By the KiDS cluster search, we expect to increase the completeness of the clusters catalogue to z = 0.6-0.7 compared to RedMapper.
In this paper, we present the tools used to search for galaxy clusters in the Kilo Degree Survey (KiDS), and our first results. The cluster detection is based on an implementation of the optimal filtering technique that enables us to identify cluster
We present the mass calibration for galaxy clusters detected with the AMICO code in KiDS DR3 data. The cluster sample comprises $sim$ 7000 objects and covers the redshift range 0.1 < $z$ < 0.6. We perform a weak lensing stacked analysis by binning th
We present the first catalogue of galaxy cluster candidates derived from the third data release of the Kilo Degree Survey (KiDS-DR3). The sample of clusters has been produced using the Adaptive Matched Identifier of Clustered Objects (AMICO) algorith
Unbiased and precise mass calibration of galaxy clusters is crucial to fully exploit galaxy clusters as cosmological probes. Stacking of weak lensing signal allows us to measure observable-mass relations down to less massive halos halos without extra
We present the first sample of 882 optically selected galaxy clusters in the Deep Lens Survey (DLS), selected with the Bayesian Cluster Finder. We create mock DLS data to assess completeness and purity rates, and find that both are at least $70%$ wit