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The existing open cluster membership determination algorithms are eitherprior dependent on some known parameters of clusters or are not automatable tolarge samples of clusters. In this paper, we present, ML-MOC, a new machinelearning based approach to identify likely members of open clusters using theGaia DR2 data, and no a priori information about cluster parameters. We use thek-Nearest Neighbours (kNN) algorithm and the Gaussian Mixture Model (GMM) onthe high-precision proper motions and parallax measurements from Gaia DR2 datato determine the membership probabilities of individual sources down to G ~20mag. 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, NGC7044, NGC 7142, NGC 752, Blanco 1, Berkeley 18, IC 4651, and Hyades. Theseclusters differ in terms of their ages, distances, metallicities, extinctionsand cover a wide parameter space in proper motions and parallaxes with respectto the field population. The extracted members produce clean colour-magnitudediagrams and our astrometric parameters of the clusters are in good agreementwith the values derived by the previous works. The estimated degree ofcontamination in the extracted members range between 2% and 12%. The resultsshow that ML-MOC is a reliable approach to segregate the open cluster membersfrom the field stars.

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