AMICO galaxy clusters in KiDS-DR3: sample properties and selection function
Matteo Maturi, Fabio Bellagamba, Mario Radovich, Mauro Roncarelli,, Mauro Sereno, Lauro Moscardini, Sandro Bardelli, Emanuella Puddu

TL;DR
This paper introduces a new catalogue of nearly 8,000 galaxy cluster candidates from KiDS-DR3, using an algorithm that avoids colour-based selection effects, and thoroughly characterizes its properties and uncertainties.
Contribution
The paper presents the first catalogue of galaxy clusters from KiDS-DR3 using the AMICO algorithm that does not rely on galaxy colours, reducing selection biases.
Findings
Catalogue contains 7988 galaxy cluster candidates.
Purity of the sample approaches 95%.
Provides probabilistic galaxy-cluster associations.
Abstract
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) algorithm. In this analysis AMICO takes advantage of the luminosity and spatial distribution of galaxies only, not considering colours. In this way, we prevent any selection effect related to the presence or absence of the red-sequence in the clusters. The catalogue contains 7988 candidate galaxy clusters in the redshift range 0.1<z<0.8 down to S/N>3.5 with a purity approaching 95% over the entire redshift range. In addition to the catalogue of galaxy clusters we also provide a catalogue of galaxies with their probabilistic association to galaxy clusters. We quantify the sample purity, completeness and the uncertainties of the detection properties,…
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