Article
STAR CLASSIFICATION AUTOMATION USING MACHINE LEARNING ON NASA DATA
Star type classification plays a crucial role in astrophysical research and space exploration. Identifying different types of stars helps in understanding stellar evolution, examining their physical characteristics, and exploring the properties of celestial bodies throughout the universe. Accurate classification supports cosmological research, improves models of stellar lifecycles, and enhances the accuracy of the Hertzsprung-Russell diagram. It also benefits practical applications such as spacecraft mission planning, telescope-based observations, and large-scale astronomical surveys through automated categorization of stars. Traditional methods for classifying star types, such as statistical techniques and decision trees, often fall short in performance. These approaches typically struggle with capturing the complex, nonlinear relationships present in astronomical data and underutilize available features. Moreover, manual feature engineering becomes inefficient and impractical when applied to extensive datasets, resulting in lower accuracy and reduced generalization to new star types. In this work, we focus on key features such as temperature, luminosity, radius, magnitude, color, spectral class, and star type labels, including Red Dwarf, Brown Dwarf, White Dwarf, Main Sequence, Super Giants, and Hyper Giants. We conduct a detailed evaluation of multiple machine learning (ML) models for star type prediction and propose an enhanced approach aimed at improving both classification accuracy and computational efficiency
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