Membership determination in open clusters using the DBSCAN Clustering Algorithm

TL;DR

This study applies the DBSCAN clustering algorithm to Gaia DR3 data to accurately identify star members in twelve open clusters, demonstrating its effectiveness across various cluster parameters and faint magnitudes.

Contribution

The paper introduces a novel application of DBSCAN for star membership determination in open clusters using Gaia data, including validation with spectroscopic data.

Findings

Reliable cluster membership identification down to G ~ 20 mag

Effective in outer cluster regions

Validated with spectroscopic data from APOGEE and GALAH

Abstract

In this paper, we apply the machine learning clustering algorithm Density Based Spatial Clustering of Applications with Noise (DBSCAN) to study the membership of stars in twelve open clusters (NGC~2264, NGC~2682, NGC~2244, NGC~3293, NGC~6913, NGC~7142, IC~1805, NGC~6231, NGC~2243, NGC 6451, NGC 6005 and NGC 6583) based on Gaia DR3 Data. This sample of clusters spans a variety of parameters like age, metallicity, distance, extinction and a wide parameter space in proper motions and parallaxes. We obtain reliable cluster members using DBSCAN as faint as $G \sim 20$ mag and also in the outer regions of clusters. With our revised membership list, we plot color-magnitude diagrams and we obtain cluster parameters for our sample using ASteCA and compare it with the catalog values. We also validate our membership sample by spectroscopic data from APOGEE and GALAH for the available data. This paper demonstrates the effectiveness of DBSCAN in membership determination of clusters.