CALSAGOS：聚类ALgorithmS在过密系统Galerkin中的应用
CALSAGOS: Clustering ALgorithmS Applied to Galaxies in Overdense Systems
摘要：
在本文中，我们提出了CALSAGOS：聚类AL出租mS适用于Galerkin在过密系统，这是一个PYTHON软件包开发的集群成员选择和搜索，找到，并确定子结构。CALSAGOS是基于聚类算法，并开发用于光谱和光度样品。为了测试CALSAGOS的性能，我们使用S-PLUS的模拟目录，我们发现在成员选择上的误差为1%-6%，这取决于所使用的函数。此外，CALSAGOS的$F_1$ -评分为0.8，识别星系团外围子结构（$r > r_{200}$）的精度为85%，完整性为100%.当我们考虑所有子结构识别（内部和外部）时，CALSAGOS的$F_1$ -得分、精度和完整性下降到0.5、75\%和40\%，这是由于搜索、发现和识别子结构的功能在2D中工作，并且不能解析投影在其他子结构上的子结构。
英文摘要：
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\% on member selection depending on the function that is used. Besides, CALSAGOS has a $F_1$-score of 0.8, a precision of 85\% and a completeness of 100\% in the identification of substructures in the outer regions of galaxy clusters ($r > r_{200}$). The $F_1$-score, precision and completeness of CALSAGOS fall to 0.5, 75\% and 40\% 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.
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