DBSCAN Clustering Algorithm for Detection of Nearby Open Clusters Based on Gaia-DR2

Abstract

In this paper, we attempt to use the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm to detect nearby open clusters based on Gaia Data Release 2 (Gaia-DR2). We select 594284 stars (within a distance of 100 pc to the sun) from the Gaia-DR2 catalog, and construct a five dimensional phase space (three dimensional space position and two dimensional proper motions) in order to obtain reliable cluster members. At the data preprocessing stage, we normalize each dimension of data to the [0,1] interval in order to avoid the effect of inconsistent units. Then, we use k-dist graph to determine the input parameters of the DBSCAN Algorithm. Finally, we obtain 133 reliable members using the DBSCAN algorithm, which correspond to two open clusters - Hyades and Coma. According to these cluster members, the distances to the Hyades and Coma clusters are determined to be (46.5 ± 0.3) pc and (84.9 ± 0.4) pc, respectively.