论文标题
Calsagos：用于过度密集系统中星系的聚类算法
CALSAGOS: Clustering ALgorithmS Applied to Galaxies in Overdense Systems
论文作者
论文摘要
在本文中，我们介绍Calsagos：应用于过度密度系统中星系的聚类算法，该算法是一个开发的Python软件包，以选择群集成员并搜索，查找和识别子结构。 Calsagos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，我们使用了S -Plus的模拟目录，并且根据使用的功能，我们发现会员选择的错误为1 \％-6 \％。此外，Calsagos的$ F_1 $ -SCORE为0.8，精度为85 \％，在识别星系簇外部区域的子结构（$ r_> r_ {200} $）的识别中的完整性为100 \％。 $ f_1 $ -score，Calsagos的精度和完整性降至0.5、75 \％和40 \％时，当我们考虑所有子结构识别（内部和外部）时，由于搜索，查找和识别子结构的功能，并且无法以2D的范围起作用，并且无法解决其他附近的子结构。
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.
