The membership of stars, density profile and mass segregation in open clusters using a new machine learning-based method

Abstract

A combination of two unsupervised machine learning algorithms, DBSCAN and GMM are used to find members with a high probability of twelve open clusters, M38, NGC2099, Coma Ber, NGC752, M67, NGC2243, Alessi01, Bochum04, M34, M35, M41, and M48, based on Gaia DR3. These clusters have different ages, distances, and numbers of members which makes a suitable cover of these parameters situation to analyze this method. We have identified 752, 1725, 116, 269, 1422, 936, 43, 38, 743, 1114, 783, and 452, probable and possible members with a higher probability than 0.8 for M38, NGC2099, Coma Ber, NGC752, M67, NGC2243, Alessi01, Bochum04, M34, M35, M41, and M48, respectively. Moreover, we obtained the tidal radius, core radius, and clear evidence of mass segregation in ten clusters. From an examination of the high-quality color-magnitude data of the cluster, we obtained one white dwarf for each of NGC752, Coma Ber and M67. In the young open cluster M38, we found all members inside the tidal radius however in the older clusters we found some members outside of the tidal radius, indicating that the young open clusters had not enough time to form clear tidal tails. It is seen that mass segregation occurs at a higher rate in older clusters than the younger ones.

26 Pages, 18 Figures, accepted by MNRAS

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Cited by in corpus (3)

• A review of unsupervised learning in astronomy
• Detection of Open Cluster Members Inside and Beyond Tidal Radius by Machine Learning Methods Based on Gaia DR3
• Cluster membership analysis with supervised learning and $N$-body simulations