Refined classification of YSOs and AGB stars by IR magnitudes, colors, and time-domain analysis with machine learning

TL;DR

This paper presents a machine learning-based binary classifier that combines infrared photometry and light curve analysis to improve the differentiation between Young Stellar Objects and AGB stars, reducing misclassification.

Contribution

The study introduces the Double Filter Model, a novel approach that integrates light curve data with IR magnitudes and colors for enhanced YSO and AGB star classification.

Findings

Successfully identified misclassified YSOs and AGB stars

Validated model against Taurus YSOs and spectroscopically confirmed AGB stars

Refined the SPICY catalog to reduce stellar misclassification

Abstract

We introduce a binary classification model, {\it 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 {\it Spitzer/IRAC Candidate YSO Catalog for the Inner Galactic Midplane} (SPICY) catalog for catalog refinement and identified potential AGB star contaminants.