Abstract
Membership of stars in open clusters is one of the most crucial parameters in studies of star clusters. Gaia opened a new window in the estimation of membership because of its unprecedented 6-D data. In the present study, we used published membership data of nine open star clusters as a training set to find new members from Gaia DR2 data using a supervised random forest model with a precision of around 90%. The number of new members found is often double the published number. Membership probability of a larger sample of stars in clusters is a major benefit in determination of cluster parameters like distance, extinction and mass functions. We also found members in the outer regions of the cluster and found sub-structures in the clusters studied. The color magnitude diagrams are more populated and enriched by the addition of new members making their study more promising.
Highlights
Star clusters are the building blocks of galaxies and are the key to understanding the formation and evolution of stars and galaxies [1] [2] [3] [4] [5]
We have used Random Forest (RF) to estimate membership of a larger sample of stars in Gaia DR2 data for nine open clusters. This kind of analysis has the following major advantages: – Our results indicate that this machine-learning-based method is highly suitable for membership determination of open clusters in high dimensional feature space. – The sample of stars in clusters can be increased by a large factor, almost 2–3 times
This improves our accuracy in determining various parameters of a star cluster ranging from distance, extinction and mass function
Summary
Star clusters are the building blocks of galaxies and are the key to understanding the formation and evolution of stars and galaxies [1] [2] [3] [4] [5]. The second Gaia data release DR2 [12][13] (and references therein) contains precise astrometry at the sub-milliarcsecond level and homogeneous three-band photometry for about 1.3 billion sources This can be used to characterize a large number of clusters over the entire sky. Using Gaia data DR2, [14] provided a list of 1229 clusters with membership data and derived parameters, in particular, mean distances and proper motions. They applied an unsupervised membership assignment code, UPMASK [15], which is primarily based on the K-means clustering algorithm to detect the cluster members and using random sampling to assign the membership probability.
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