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Machine Learning in the study of Star Clusters with Gaia EDR3

Published online by Cambridge University Press:  01 August 2025

Priya Hasan Open the ORCID record for Priya Hasan [Opens in a new window] ,
Md Mahmudunnobe ,
Mudasir Raja ,
Md Saifuddin  and
S N Hasan
Priya Hasan*
Affiliation:
Department of Physics, Maulana Azad National Urdu University, Hyderabad 500032, India
Md Mahmudunnobe
Affiliation:
Department of Physics, Minerva University, San Francisco, California 94103, USA
Mudasir Raja
Affiliation:
Department of Physics, Maulana Azad National Urdu University, Hyderabad 500032, India
Md Saifuddin
Affiliation:
Department of Physics, Maulana Azad National Urdu University, Hyderabad 500032, India
S N Hasan
Affiliation:
Department of Mathematics, Maulana Azad National Urdu University, Hyderabad 500032, India
*
email: priya.hasan@gmail.com
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Abstract

Determination of membership of star clusters is a very important criterion in their study as they effect determination of cluster parameters like radius, age, distance, mass functions, etc. In an earlier 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 (RF) model with a precision of around 90%. The number of new members found was almost double the published number. In this work, we would like to compare the earlier results with results obtained by applying the unsupervised method of Gaussian Mixture Modelling (GMM) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to study the membership of open star clusters of varying ages and locations in the Galaxy using Gaia DR2 and EDR3 data. We shall discuss these techniques and focus on the caveats involved.

Keywords

Machine LearningGMMGaia EDR3Star ClustersMembership

Information

Type
Poster Paper
Information
Proceedings of the International Astronomical Union , Volume 19 , Symposium S368: Machine Learning in Astronomy: Possibilities and Pitfalls , August 2023 , pp. 86 - 90
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of International Astronomical Union

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