DBSCAN Clustering Algorithm for the Detection of Nearby Open Clusters Based on Gaia-DR2two

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 the 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 spatial position and two-dimensional proper motion) 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 the k-dist graphs 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 (6.5 ± 0.3) pc and (4.9 ± 0.4) pc, respectively.