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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 extrapolation. We propose a Bayesian inference method to constrain the intrinsic scatter of the mass proxy in stacked analyses. The scatter of the stacked data is rescaled with respect to the individual scatter based on the number of binned clusters. We apply this method to the galaxy clusters detected with the AMICO (Adaptive Matched Identifier of Clustered Objects) algorithm in the third data release of the Kilo-Degree Survey. The results confirm the optical richness as a low scatter mass proxy. Based on the optical richness and the calibrated weak lensing mass-richness relation, mass of individual objects down to ~10^13 solar masses can be estimated with a precision of ~20 per cent.
We present a cosmological analysis of abundances and stacked weak-lensing profiles of galaxy clusters, exploiting the AMICO KiDS-DR3 catalogue. The sample consists of 3652 galaxy clusters with intrinsic richness $lambda^*geq20$, over an effective are
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
Context. The large-scale mass distribution around dark matter haloes hosting galaxy clusters provides sensitive cosmological information. Aims. In this work, we make use of a large photometric galaxy cluster sample, constructed from the public Third
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
A catalogue of galaxy clusters was obtained in an area of 414 sq deg up to a redshift $zsim0.8$ from the Data Release 3 of the Kilo-Degree Survey (KiDS-DR3), using the Adaptive Matched Identifier of Clustered Objects (AMICO) algorithm. The catalogue