Machine Learning Approaches for Classifying Star-Forming Galaxies and Active Galactic Nuclei from MIGHTEE-Detected Radio Sources in the COSMOS Field

Abstract

Radio synchrotron emission originates from both massive star formation and black hole accretion, two processes that drive galaxy evolution. Efficient classification of sources dominated by either process is therefore essential for fully exploiting deep, wide-field extragalactic radio continuum surveys. In this study, we implement, optimize, and compare five widely used supervised machine-learning (ML) algorithms to classify radio sources detected in the MeerKAT International GHz Tiered Extragalactic Exploration (MIGHTEE)-COSMOS survey as star-forming galaxies (SFGs) and active galactic nuclei (AGN). Training and test sets are constructed from conventionally classified MIGHTEE-COSMOS sources, and 18 physical parameters of the MIGHTEE-detected sources are evaluated as input features. As anticipated, our feature analyses rank the five parameters used in conventional classification as the most effective: the infrared-radio correlation parameter ($q_\mathrm{IR}$), the optical compactness morphology parameter (class$\_$star), stellar mass, and two combined mid-infrared colors. By optimizing the ML models with these selected features and testing classifiers across various feature combinations, we find that model performance generally improves as additional features are incorporated. Overall, all five algorithms yield an $F1$-score (the harmonic mean of precision and recall) $>90\%$ even when trained on only $20\%$ of the dataset. Among them, the distance-based $k$-nearest neighbors classifier demonstrates the highest accuracy and stability, establishing it as a robust and effective method for classifying SFGs and AGN in upcoming large radio continuum surveys.