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∼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.
@article{arxiv.2404.10477,
title = {Membership determination in open clusters using the DBSCAN Clustering Algorithm},
author = {Mudasir Raja and Priya Hasan and Md Mahmudunnobe and Md Saifuddin and S N Hasan},
journal= {arXiv preprint arXiv:2404.10477},
year = {2024}
}
Comments
Accepted in Astronomy and Computing. arXiv admin note: text overlap with arXiv:2401.10802