ItemConference Paper

Advancing Astronomical Science - Machine Learning-Based Classification of Variable Stars for Scientific Innovation and Research

Title
Advancing Astronomical Science - Machine Learning-Based Classification of Variable Stars for Scientific Innovation and Research
Creator
Oommen, Julia; Ramasamy, Gobi
Description
Variable star classification is an important part of astrophysics and gives astrophysicists a way of studying stellar evolution, structure and dynamics. Due to the availability of large scale surveys such as Gaia DR3, machine learning techniques are used in automation of the classification process. In this study, RF, SVM, MLP and XGBoost (XGBClassifier) models are evaluated for classification of variable stars. The data set used in this work was collected from Gaia DR3 using Astroquery and the ability of these models is evaluated for different star classes. The result shows that the XGBoost had the best accuracy of 91% compared to RF (89.98%), MLP (88%) and SVM (83%). A comparison of various metrics such as precision, recall and F1-score of each method is also provided to address their strengths and weaknesses. This work further emphasises the need of sophisticated machine learning techniques in astrophysical data analysis and discusses problems of certain kinds of variable star classification. KeywordsGaia, variable stars, Classification, Machine Learning, Random Forest, XGBoost, Multi-Layer Perceptron, Support Vector Machine. 2025 IEEE.
Source
Proceedings of 2025 International Conference on Emerging Technologies in Computing and Communication, ETCC 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Classification; Gaia; Machine Learning; Multi-Layer Perceptron; Random Forest; Support Vector Machine; variable stars; XGBoost
Coverage
Oommen J., Department of Computer Science, Christ (Deemed to be University), Bangalore, India; Ramasamy G., Department of Computer Science, Christ (Deemed to be University), Bangalore, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833152476-0;
Format
online
Language
English
Type
Conference paper

Citation

Oommen, Julia; Ramasamy, Gobi, “Advancing Astronomical Science - Machine Learning-Based Classification of Variable Stars for Scientific Innovation and Research,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25834.

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