Enhancing Stellar Temperature Estimation through Machine Learning and Multifaceted Data Exploration

Abstract This paper employs machine learning to estimate stellar temperatures using photometric data, focusing on the GAIA ESA Archive Data Release 3 data set. The study underscores the effectiveness of neural networks in deciphering intricate relationships within the data. Notably, the addition of metallicity improves model accuracy in characterizing stellar properties. The study also investigates outlier removal techniques, specifically favoring the Isolation Forest method, showcasing its efficacy in refining model performance. Automated machine learning, facilitated by PyCaret Regressor, emerges as a valuable tool, identifying top-performing models and highlighting feature importance. The implications of this research extend beyond the specifics of stellar temperature estimation. In contemplating future directions, this study suggests the exploration of diverse data sources to ensure balanced distributions of stellar temperatures and the potential incorporation of deep learning architectures for heightened accuracy in addressing astrophysical inquiries.