基于DBSCAN聚類算法的疏散星團NGC 188的3維運動學成員判定
發(fā)布時間：2018-11-26 08:44
【摘要】：利用疏散星團NGC 188所在天區(qū)的1046顆恒星樣本的高精度3維(3D)運動學數(shù)據(jù)(自行和視向速度)測試了DBSCAN(Density-Based Spatial Clustering of Applications with Noise)聚類算法的成員判定效果.為了避免自行和視向速度的單位不一致帶來的影響,在數(shù)據(jù)預處理階段將3個分量的數(shù)據(jù)統(tǒng)一標準化至[0,1]區(qū)間.利用第k個最近鄰點距離方法分析了1046顆恒星樣本在標準化無量綱3D速度空間的分布特征,再根據(jù)第k個最近鄰點距離隨k值的變化趨勢確定了DBSCAN聚類算法的輸入?yún)?shù)(Eps,MinPts),最后利用DBSCAN聚類算法分離出497顆3D運動學成員星.分析結(jié)果表明得到的3D運動學成員星是可靠的.
[Abstract]:The high accuracy 3D kinematics data (self-motion and apparent velocity) of 1046 star samples in the sky region of open cluster NGC 188 are used to test the membership determination effect of DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm). In order to avoid the effect of the unit inconsistency of self-motion and apparent velocity, the data of three components are standardized to the range of [0 / 1] in the stage of data preprocessing. The distribution characteristics of 1046 star samples in dimensionless 3D velocity space are analyzed by using the k-th nearest neighbor distance method, and the input parameters of DBSCAN clustering algorithm (Eps,) are determined according to the variation trend of the k-th nearest neighbor distance with k value. MinPts), finally uses DBSCAN clustering algorithm to separate 497 3D kinematics stars. The results show that the 3D kinematics star is reliable.
【作者單位】： 常州大學信息科學與工程學院;
【基金】：國家自然科學基金項目(11403004)資助
【分類號】：P154.11
，
本文編號：2358017
[Abstract]:The high accuracy 3D kinematics data (self-motion and apparent velocity) of 1046 star samples in the sky region of open cluster NGC 188 are used to test the membership determination effect of DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm). In order to avoid the effect of the unit inconsistency of self-motion and apparent velocity, the data of three components are standardized to the range of [0 / 1] in the stage of data preprocessing. The distribution characteristics of 1046 star samples in dimensionless 3D velocity space are analyzed by using the k-th nearest neighbor distance method, and the input parameters of DBSCAN clustering algorithm (Eps,) are determined according to the variation trend of the k-th nearest neighbor distance with k value. MinPts), finally uses DBSCAN clustering algorithm to separate 497 3D kinematics stars. The results show that the 3D kinematics star is reliable.
【作者單位】： 常州大學信息科學與工程學院;
【基金】：國家自然科學基金項目(11403004)資助
【分類號】：P154.11
，
本文編號：2358017
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