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Is GMM effective in membership determination of Open Clusters?

Published online by Cambridge University Press:  01 August 2025

S. N. Hasan ,
Md Mahmudunnobe ,
Priya Hasan Open the ORCID record for Priya Hasan [Opens in a new window]  and
Mudasir Raja
S. N. Hasan*
Affiliation:
Department of Mathematics, Maulana Azad National Urdu University, Hyderabad 500032, India
Md Mahmudunnobe
Affiliation:
Department of Physics, Minerva University, San Francisco, California 94103, USA
Priya Hasan*
Affiliation:
Department of Physics, Maulana Azad National Urdu University, Hyderabad 500032, India
Mudasir Raja
Affiliation:
Department of Physics, Maulana Azad National Urdu University, Hyderabad 500032, India
*
email: hasan.najam@gmail.com
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 this paper, we apply the unsupervised method of Gaussian Mixture Modelling (GMM) to find membership of 9 open star clusters of varying ages and locations in the galaxy using Gaia DR3 data. We compare our results to help understand the efficiency of GMM. We find that this method works well with relaxed clusters with ages larger than their relaxation times they approximate Gaussians better.

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. 125 - 126
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of International Astronomical Union

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References

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