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