We present AMICO (Adaptive Matched Identifier of Clustered Objects), a new algorithm for the detection of galaxy clusters in photometric surveys. AMICO is based on the Optimal Filtering technique, which allows to maximise the signal-to-noise ratio of the clusters. In this work we focus on the new iterative approach to the extraction of cluster candidates from the map produced by the filter. In particular, we provide a definition of membership probability for the galaxies close to any cluster candidate, which allows us to remove its imprint from the map, allowing the detection of smaller structures. As demonstrated in our tests, this method allows the deblending of close-by and aligned structures in more than 50% of the cases for objects at radial distance equal to 0.5×R200 or redshift distance equal to 2×σz, being σz the typical uncertainty of photometric redshifts. Running AMICO on mocks derived from N-body simulations and semi-analytical modelling of the galaxy evolution, we obtain a consistent mass-amplitude relation through the redshift range 0.3<z<1, with a logarithmic slope ∼0.55 and a logarithmic scatter ∼0.14. The fraction of false detections is steeply decreasing with S/N, and negligible at S/N > 5.
@article{arxiv.1705.03029,
title = {AMICO: optimised detection of galaxy clusters in photometric surveys},
author = {Fabio Bellagamba and Mauro Roncarelli and Matteo Maturi and Lauro Moscardini},
journal= {arXiv preprint arXiv:1705.03029},
year = {2017}
}