CALSAGOS: Clustering algorithms applied to galaxies in overdense systems

Abstract

ABSTRACT In this paper, we present CALSAGOS: Clustering ALgorithmS Applied to Galaxies in Overdense Systems which is a PYTHON package developed to select cluster members and to search, find, and identify substructures. CALSAGOS is based on clustering algorithms, and was developed to be used in spectroscopic and photometric samples. To test the performance of CALSAGOS, we use the S-PLUS’s mock catalogues, and we found an error of 1–6 per cent on member selection depending on the function that is used. Besides, CALSAGOS has a F1-score of 0.8, a precision of 85 per cent and a completeness of 100 per cent in the identification of substructures in the outer regions of galaxy clusters (r > r200). The F1-score, precision, and completeness of CALSAGOS fall to 0.5, 75, and 40 per cent when we consider all substructure identifications (inner and outer) due to the function that searches, finds, and identifies the substructures works in 2D, and cannot resolve the substructures projected over others.