Uncovering Stellar Patterns and Anomalies Through Data-Driven Astronomy

Abstract

Abstract This paper explores data-driven methodologies in astronomy, leveraging large-scale datasets to investigate stellar properties, classification patterns, and potential anomalies in stellar evolution. Using machine learning and statistical tools, we analyze datasets from large sky surveys, including Gaia and SDSS, focusing on identifying unique features that traditional methods may overlook. Our findings demonstrate that data-driven approaches can uncover nuanced stellar behaviors and improve classification systems. Keywords: Data-driven astronomy, machine learning, stellar classification, anomaly detection, sky surveys.