Abstract Details

Name: Manan Agarwal
Affiliation: Birla Institute of Technology and Science, Pilani
Conference ID: ASI2021_146
Title : ML-MOC: Machine Learning based Membership Determination for Open Clusters
Authors and Co-Authors : Manan Agarwal (Birla Institute of Technology and Science, Pilani), Khushboo K. Rao (Birla Institute of Technology and Science, Pilani), Kaushar Vaidya (Birla Institute of Technology and Science, Pilani), Souradeep Bhattacharya (Inter University Centre for Astronomy and Astrophysics)
Abstract Type : Poster
Abstract Category : Instrumentation and Techniques
Abstract : Open Clusters are the ideal laboratories to study the formation and evolution of stars as they provide us chemically homogeneous groups of stars that are of the same age, share the same kinematics (proper motion and radial velocity), and are located at approximately the same given distance from us. Accurate membership determination of open clusters is crucial to their studies as it directly influences the estimation of the fundamental physical parameters of clusters. We present a new machine learning based algorithm, ML-MOC, to identify members of open clusters using the Gaia DR2 data (and now using EDR3 as well). Our algorithm uses the combination of k-Nearest Neighbours algorithm and the Gaussian Mixture Model on the high-precision proper motions and parallax measurements from Gaia data to determine the membership probabilities of individual sources down to G ~20 mag. To validate the developed method, we have applied it on thirteen open clusters: M67, NGC 2099, NGC 2141, NGC 2243, NGC 2539, NGC 6253, NGC 6405, NGC 6791, NGC 7044, NGC 7142, NGC 752, Berkeley 18, and IC 4651. 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 results show that our method is a reliable approach to segregate the open cluster members from the field stars.