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arXiv · 1810.02811

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
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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 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, such as richness, redshift, and position, by means of mock galaxy catalogues derived directly from the data. This preserves their statistical properties including photo-z uncertainties, unknown absorption across the survey, missing data, spatial correlation of galaxies and galaxy clusters. Being based on the real data, such mock catalogues do not have to rely on the assumptions on which numerical simulations and semi-analytic models are based on. This paper is the first of a series of papers in which we discuss the details and physical properties of the sample presented in this work.

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Matteo Maturi, Fabio Bellagamba, Mario Radovich, Mauro Roncarelli, Mauro Sereno, Lauro Moscardini, Sandro Bardelli, Emanuella Puddu. 2018-10-05. AMICO galaxy clusters in KiDS-DR3: sample properties and selection function. https://doi.org/10.1093/mnras%2Fstz294

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