Spectral classification of OB-type stars using tree-based ensemble methods and evaluation of their explainability

Abstract

[Abridged] The advent of large-scale spectroscopic surveys will deliver tens of thousands of spectra of blue massive stars. This data volume renders traditional spectral classification techniques increasingly impractical. We aim to develop a robust ML framework for the automated spectral classification of massive OB stars by assessing several tree-based ensemble algorithms. Using a large catalog of high-quality optical spectra of OB stars, we design 3 hierarchical experiments of increasing complexity: classification into broad SpT; fine-grained classification within the O and B domains; and joint classification of SpT and LC. We evaluate 6 algorithms, ranging from single Decision Trees to ensemble methods and gradient boosting techniques. We further analyze model consensus and probabilistic outputs, and employ SHAP value analysis to provide an interpretation of the model decisions by identifying the features that drive each classification. Models achieve excellent performance for broad SpT classification, with LGBM reaching an accuracy of 98%. For fine-grained SpT, RF provides the most robust results (89\%). For the most challenging task combining SpT subtype and LC the XGB reaches a 77% accuracy. Decrease in performance is primarily driven by intrinsic degeneracies in the classical spectral classification scheme. SHAP analysis confirms that the models rely on meaningful spectral features. We also find a high level of agreement among different algorithms, indicating that current performance limits are largely set by the intrinsic complexity of the classification problem rather than by model choice. Tree-based ensemble methods provide a reliable and interpretable framework for the automated spectral classification of OB stars. The combination of full-spectrum modeling, algorithm comparison, and model interpretability offers a robust approach for future large-scale spectroscopic surveys.