Membership of Stars in Open Clusters using Random Forest with Gaia Data

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.

arXiv commentAccepted for publication in The European Physical Journal ST, Special Issue on Modeling Machine Learning and Astronomy

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• Stellar population of the Rosette Nebula and NGC 2244: application of the probabilistic random forest
• Membership analysis and 3D kinematics of the star-forming complex around Trumpler 37 using Gaia-DR3
• Mass and wind luminosity of young Galactic open clusters in Gaia DR2
• Cluster membership analysis with supervised learning and $N$-body simulations
• The frequency by mass of Galactic carbon stars inferred from Gaia measurements of star cluster members