Physics-guided Deep Learning for the Classification of Wolf–Rayet Stars in DESI

Abstract Wolf–Rayet (W-R) stars, which represent a key evolutionary stage of massive stars, exhibit strong stellar winds and broad emission lines. These features serve as direct tracers of stellar feedback and chemical enrichment in star-forming regions. Utilizing the recently released Dark Energy Spectroscopic Instrument (DESI) Data Release 1, we conduct a systematic search for W-R stars. To enhance physical interpretability while achieving high performance in the survey data, we introduce an improved model, LPA-MLK-CNN, which combines two key innovations: (i) a line-prior attention (LPA) mechanism that emphasizes diagnostic W-R emission lines by embedding astrophysical priors within a data-driven framework; (ii) a multiscale large-kernel convolutional neural network (MLK-CNN) structure that captures diverse emission-line features across stellar types. Trained on spectra from a six-class dataset, the model achieves significantly improved performance compared to conventional machine learning and deep learning baselines. When applied to the DESI spectral data, LPA-MLK-CNN recovers two newly discovered W-R candidates, contributing to the expansion of the W-R star catalog.