【摘要】 成员判定是疏散星团研究中最关键的一步,成员判定的好坏直接影响对星团基本参数的估计.首次利用数据挖掘技术中的DBSCAN(Density-Based Spatial Clustering of Applications with Noise)聚类算法对疏散星团NGC 6791和M67(NGC 2682)分别进行了成员判定,结果表明DBSCAN聚类算法能很有效地剔除场星污染.得到的NGC6791成员星的颜色-星等图上主序清晰并呈现明显的双重主序结构,这表明NGC 6791可能有更复杂的恒星形成与演化历史.对M67的分析表明出现了质量分层现象,并且星团的核心和外围两部分有明显的相对运动.对NGC 6791和M67的分析均表明DBSCAN聚类算法是一种有效的成员判定方法,有传统成员判定方法不具备的一些优点.更多还原

【Abstract】 Membership determination is the most important step to study the open clusters,which can directly influence the accuracy of open clusters’ physical parameters.DBSCAN(Density-Based Spatial Clustering of Applications with Noise) is one of the most popular density-based clustering algorithm,which can find arbitrarily shaped clusters in databases.DBSCAN algorithm has been used for the first time to select probable members of the open clusters NGC 6791 and M67(NGC 2682).Our analysis results indicate that DBSCAN algorithm has some advantages over the widely used kinematic methods.DBSCAN algorithm can effectively select cluster members without any hypothesis about the propermotion distribution.DBSCAN algorithm can also select probable members only based on the color-magnitude diagram.We confirm the presence of a double main sequence based on the cleaned color-magnitude diagram of NGC 6791.Analysis of the proper motions of M67reveals a distinct relative motion between the core of the cluster and its periphery.更多还原