论文标题
银河光环的分层结构：clustar-nd的广义n维聚类
The Hierarchical Structure of Galactic Haloes: Generalised N-Dimensional Clustering with CluSTAR-ND
论文作者
论文摘要
我们提出了Clustar-nd，这是一个快速的层次星系/（sub）Halo Finder，可通过{\ bf t} ransformative {\ bf t} ransformative {\ bf a} Ggregation和{\ bf r} exoture {\ bf bf n} i {它旨在改善晕旋转元素（一种算法，该算法自动从模拟粒子的3D空间位置自动检测并提取了重要的天体物理簇 - 通过降低运行时间，具有指标适应性的能力，并易于适用于具有任何特征的数据。我们直接比较了这些算法，发现Clustar-ND不仅产生了类似稳健的聚类结构，而且在运行时至少要快$ 3 $的数量级。在优化Clustar-ND的聚类性能时，我们还仔细地校准了$ 7 $ Clustar-ND参数的$ 4 $，除非用户指定，否则将根据输入数据自动且最佳地选择。我们得出的结论是，Clustar-ND是一种可靠的天体聚集算法，可以利用大型合成或观察数据集中的恒星卫星组。
We present CluSTAR-ND, a fast hierarchical galaxy/(sub)halo finder that produces {\bf Clu}stering {\bf S}tructure via {\bf T}ransformative {\bf A}ggregation and {\bf R}ejection in {\bf N}-{\bf D}imensions. It is designed to improve upon Halo-OPTICS -- an algorithm that automatically detects and extracts significant astrophysical clusters from the 3D spatial positions of simulation particles -- by decreasing run-times, possessing the capability for metric adaptivity, and being readily applicable to data with any number of features. We directly compare these algorithms and find that not only does CluSTAR-ND produce a similarly robust clustering structure, it does so in a run-time that is at least $3$ orders of magnitude faster. In optimising CluSTAR-ND's clustering performance, we have also carefully calibrated $4$ of the $7$ CluSTAR-ND parameters which -- unless specified by the user -- will be automatically and optimally chosen based on the input data. We conclude that CluSTAR-ND is a robust astrophysical clustering algorithm that can be leveraged to find stellar satellite groups on large synthetic or observational data sets.
