标题： 基于DBSCAN聚类算法的疏散星团成员判定 显示英文标题

标题： Membership determination in open clusters using the DBSCAN Clustering Algorithm

摘要： 本文中，我们应用机器学习聚类算法基于盖亚DR3数据研究了十二个疏散星团（NGC 2264、NGC 2682、NGC 2244、NGC 3293、NGC 6913、NGC 7142、IC 1805、NGC 6231、NGC 2243、NGC 6451、NGC 6005和NGC 6583）中的成员星。这一星团样本涵盖了多种参数，如年龄、金属丰度、距离、消光以及自行和视差的宽广参数空间。利用DBSCAN算法，我们获得了亮度低至$G \sim 20$量级且位于星团外围区域的可靠成员星。通过修订后的成员星列表，我们绘制了色星等图，并使用ASteCA获得样本星团的参数并与目录值进行了比较。此外，我们还通过APOGEE和GALAH的光谱数据验证了我们的成员星样本。本文展示了DBSCAN在星团成员确定中的有效性。 显示英文摘要

摘要： In this paper, we apply the machine learning clustering algorithm Density Based Spatial Clustering of Applications with Noise (DBSCAN) to study the membership of stars in twelve open clusters (NGC~2264, NGC~2682, NGC~2244, NGC~3293, NGC~6913, NGC~7142, IC~1805, NGC~6231, NGC~2243, NGC 6451, NGC 6005 and NGC 6583) based on Gaia DR3 Data. This sample of clusters spans a variety of parameters like age, metallicity, distance, extinction and a wide parameter space in proper motions and parallaxes. We obtain reliable cluster members using DBSCAN as faint as $G \sim 20$ mag and also in the outer regions of clusters. With our revised membership list, we plot color-magnitude diagrams and we obtain cluster parameters for our sample using ASteCA and compare it with the catalog values. We also validate our membership sample by spectroscopic data from APOGEE and GALAH for the available data. This paper demonstrates the effectiveness of DBSCAN in membership determination of clusters.