ML-MOC: Machine Learning (kNN and GMM) based Membership Determination for Open Clusters

TL;DR

ML-MOC is a machine learning approach using kNN and GMM on Gaia DR2 data that accurately identifies open cluster members without prior cluster parameter knowledge, producing clean diagrams and low contamination rates.

Contribution

This paper introduces ML-MOC, a novel machine learning method for open cluster membership determination that operates without prior cluster information and is applicable to large datasets.

Findings

Successfully applied to 15 diverse open clusters.

Achieved contamination rates between 2% and 12%.

Produced consistent astrometric parameters with previous studies.

Abstract

The existing open cluster membership determination algorithms are either prior dependent on some known parameters of clusters or are not automatable to large samples of clusters. In this paper, we present, ML-MOC, a new machine learning based approach to identify likely members of open clusters using the Gaia DR2 data, and no a priori information about cluster parameters. We use the k-Nearest Neighbours (kNN) algorithm and the Gaussian Mixture Model (GMM) on the high-precision proper motions and parallax measurements from Gaia DR2 data to determine the membership probabilities of individual sources down to G ~20 mag. To validate the developed method, we apply it on fifteen open clusters: M67, NGC 2099, NGC 2141, NGC 2243, NGC 2539, NGC 6253, NGC 6405, NGC 6791, NGC 7044, NGC 7142, NGC 752, Blanco 1, Berkeley 18, IC 4651, and Hyades. These clusters differ in terms of their ages, distances, metallicities, extinctions and cover a wide parameter space in proper motions and parallaxes with respect to the field population. The extracted members produce clean colour-magnitude diagrams and our astrometric parameters of the clusters are in good agreement with the values derived by the previous works. The estimated degree of contamination in the extracted members range between 2% and 12%. The results show that ML-MOC is a reliable approach to segregate the open cluster members from the field stars.