Explainable Machine Learning Classification of Chandra X-Ray Sources: SHAP Analysis of Multiwavelength Features

Abstract Extensive astronomical surveys, like those conducted with the Chandra X-ray Observatory, detect hundreds of thousands of unidentified cosmic sources. Machine learning (ML) methods offer an efficient, probabilistic approach to classifying them, which can be useful for making discoveries and conducting deeper studies. In earlier work, we applied the LightGBM (ML model) to classify 277,069 Chandra point sources into eight categories: active galactic nuclei (AGNs), X-ray emitting stars, young stellar objects (YSO), high-mass X-ray binaries, low-mass X-ray binaries, ultraluminous X-ray sources, cataclysmic variables, and pulsars. In this work, we present the classification table of 54,770 robustly classified sources (over 3 σ confidence), including 14,066 sources at >4 σ significance. To ensure classification reliability and gain a deeper insight, we investigate the multiwavelength feature relationships learned by the LightGBM model, focusing on AGNs, stars, and YSOs. We employ explainable artificial intelligence (XAI) techniques, specifically, Shapley Additive Explanations, to quantify the contribution of individual features and their interactions to the predicted classification probabilities. Among other things, we find infrared-optical and X-ray decision boundaries for separating AGN/stars, and infrared-X-ray boundaries for YSOs. These results are crucial for estimating object classes even with limited multiwavelength data. This study represents one of the earliest applications of XAI to large-scale astronomical data sets, demonstrating ML models’ potential for uncovering physically meaningful patterns in data in addition to classification. Finally, our publicly available, extensive, and interactive catalog will be helpful to explore the contributions of features and their combinations in greater detail in the future.