Galaxy clusters in the VIDEO fields: Detection and characterisation in the context of MOONRISE

The identification and characterisation of galaxy clusters at different cosmic times are fundamental to constrain cosmological scenarios and to understand the joint evolution of structure formation and galaxies in dense environments. In this work, we analyse the cluster content of the ∼ 4.5 ^ deg 2 XMM-LSS and CDFS VIDEO fields, which are expected to be partially covered by the upcoming MOONRISE survey. Using AMICO and WaZP photometric redshift-based cluster finders, we constructed a sample of 519 cluster candidates detected by both finders in the redshift range z = 0.1-3, including 74 detections at z > 1.5. For all detections, we identifed the brightest central galaxy (BCG) and computed a list of probabilistic cluster memberships. Our photometric redshift measurements of the clusters agree well with spectroscopic redshifts from the literature, when available. From ancillary spectroscopic data, we assign z_ measurements to 116 cluster candidates based on their spectroscopic members and to 204 based on their likely BCGs. We also show that candidates containing radio-loud members were efficiently recovered using the prior-based cluster finder PPM. We perform a preliminary analysis of the galaxy content of these candidates, focusing on the red-sequence components of their apparent colour-magnitude diagram. By comparing their mean colours with models of galaxy evolution, we show that this population is consistent with a model of passive evolution with a formation at high redshift, and is already in place at z = 1.5-2.0. Finally, our cluster sample was used to evaluate how these clusters would be detected and characterised, according to various MOONRISE strategies. We show that cluster spectroscopic confirmation and characterisation could be efficiently achieved up to z even with the shallowest survey strategy. This brings unprecedented insight into the physical properties of high-redshift galaxy clusters and into galaxy formation in dense environments.