Refined Classification of Young Stellar Objects and Asymptotic Giant Branch Stars by Infrared Magnitudes, Colors, and Time-domain Analysis with Machine Learning

Abstract We introduce a binary classification model, the double filter model, utilizing various machine learning and deep learning methods to classify young stellar objects (YSOs) and asymptotic giant branch (AGB) stars. Since YSOs and AGB stars share similar infrared (IR) photometric characteristics due to comparable temperatures and the presence of circumstellar dust, distinguishing them is challenging and often leads to misclassification. While machine learning and deep learning techniques have helped reduce YSO–AGB misclassifications, achieving a reliable separation remains challenging. Given that YSOs and AGB stars exhibit distinct light curves resulting from different variability mechanisms, our double filter model leverages light-curve data to enhance classification accuracy. This approach uncovered YSOs and AGB stars that were misclassified in IR photometry, and was validated against Taurus YSOs and spectroscopically confirmed AGB stars. We applied the model to the Spitzer/IRAC Candidate YSO catalog for the inner Galactic midplane for catalog refinement and identified potential AGB star contaminants.