Automated classification of celestial objects using machine learning

Muhamad Suwaid, Muhammad Aiman Haris and Ab Karim, Muhammad ‘Ilyas Amierrullah and Hassan, Raini and Abdul Aziz, Azni (2025) Automated classification of celestial objects using machine learning. International Journal on Perceptive and Cognitive Computing, 11 (2). pp. 22-41. E-ISSN 2462-229X

Preview

PDF - Published Version Download (6MB) | Preview

Abstract

The swift expansion of astronomical data requires the automated classification of celestial objects for practical use. Because of its manual and monotonous nature, classification is more prone to errors and is rapidly losing its viability. This study performs the classification of stars, galaxies, and quasars from SDSS (Sloan Digital Sky Survey) data using the Random Forest, XGBoost, Decision Tree, Gradient Boosting, Linear SVM, KNN, and Logistic Regression. In order to fix the imbalance in the data, the SMOTE algorithm was used, making the model more robust. Random Forest topped the models with their accuracy and reliability across many multiple data releases, hitting an astonishing 99.12% accuracy in SDSS DR18. This work shows how much machine deep learning can change astronomical surveys, providing readily available, precise techniques that are much more effective than manual approaches. The results add towards the development of astrophysics while simultaneously meeting Sustainable Development Goal 9 on fostering innovation through the need for infrastructure

Item Type:	Article (Journal)
Uncontrolled Keywords:	SDSS, Astronomy, Machine Learning, Random Forest, Classification
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Science > Department of Physics
Depositing User:	Dr. Raini Hassan
Date Deposited:	05 Aug 2025 10:24
Last Modified:	05 Aug 2025 10:24
URI:	http://irep.iium.edu.my/id/eprint/122465

Actions (login required)

View Item