Membership determination in open clusters using the DBSCAN Clustering Algorithm

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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Astronomy and computing Ročník 47; s. 100826
Hlavní autoři: Raja, M., Hasan, P., Mahmudunnobe, Md, Saifuddin, Md, Hasan, S.N.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.04.2024
Témata:
(Galaxy: ) open clusters and associations: general, individual
(Stars: ) Hertzsprung–Russell and color-magnitude diagrams
DBSCAN
Machine learning
DBSCAN
(Stars: ) Hertzsprung–Russell and color-magnitude diagrams
(Galaxy: ) open clusters and associations: general, individual
Machine learning
ISSN:2213-1337, 2213-1345
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
ISSN:2213-1337
2213-1345
DOI:10.1016/j.ascom.2024.100826