摘要
利用疏散星团NGC 188所在天区的1046颗恒星样本的高精度3维(3D)运动学数据(自行和视向速度)测试了DBSCAN(Density-Based Spatial Clustering of Applications with Noise)聚类算法的成员判定效果.为了避免自行和视向速度的单位不一致带来的影响,在数据预处理阶段将3个分量的数据统一标准化至[0,1]区间.利用第k个最近邻点距离方法分析了1046颗恒星样本在标准化无量纲3D速度空间的分布特征,再根据第k个最近邻点距离随k值的变化趋势确定了DBSCAN聚类算法的输入参数(Eps,MinPts),最后利用DBSCAN聚类算法分离出497颗3D运动学成员星.分析结果表明得到的3D运动学成员星是可靠的.
In order to obtain clean cluster members in the three-dimensional (3D) velocity space (radial velocity and proper motion), we construct a standardized dimen- sionless 3D velocity space. We use the k-th nearest neighbor distance (kNND) method to estimate the input parameters of the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm based on the assumption that the kNNDs of cluster members slowly increase with increasing the k value. Finally, we use the DB- SCAN clustering algorithm to obtain 497 candidate members, which are very likely the cluster members.
作者
高新华
王超
顾晓清
徐守坤
GAO Xin-hua WANG Chao GU Xiao-qing XU Shou-kun(School of Information Science and Engineering, Changzhou University, Changzhou 213164)
出处
《天文学报》
CSCD
北大核心
2017年第5期65-72,共8页
Acta Astronomica Sinica
基金
国家自然科学基金项目(11403004)资助
关键词
疏散星团和星协
个别
NGC
188
恒星
运动学与动力学
技术
视向速度
赫罗图与颜色-星等图
open clusters and associations: individual: NGC 188, stars: kinematics and dynamics, techniques: radial velocities, Hertzsprung-Russell (HR) and C-M diagrams