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

TL;DR

This study employs a supervised random forest model on Gaia DR2 data to identify new star cluster members, significantly enhancing membership catalogs and enabling better analysis of cluster properties.

Contribution

It introduces a novel application of random forest with Gaia data to improve star cluster membership identification, often doubling known members.

Findings

Achieved around 90% precision in identifying new members.

Discovered members in outer regions and sub-structures of clusters.

Enhanced color-magnitude diagrams with additional members.

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.