2026ApJ..1000...14F

While significant advances have been made in photometric classification ahead of the millions of transient events and hundreds of supernovae (SNe) each night that the Vera C. Rubin Observatory Legacy Survey of Space and Time will discover, classifying SNe spectroscopically remains the best way to determine most subtypes of SNe. Traditional spectrum classification tools use template matching techniques and require significant human supervision. Two deep learning spectral classifiers, DASH and SNIascore, define the state of the art, but SNIascore is a binary classifier devoted to maximizing the purity of the Type Ia SN (SN Ia)-norm sample. DASH is no longer maintained, and the original work suffers from contamination of multiepoch spectra in the training and test sets. We have explored several neural network architectures in order to create a new automated method for classifying SN subtypes, settling on an attention-based model we call ABC-SN. We benchmark our results against an updated version of DASH, thus providing the community with an up-to-date general-purpose SN classifier. Our dataset comprises 10 different SN subtypes including subtypes of SN Ia, core collapse, and interacting SNe. We find that ABC-SN outperforms DASH, for nearly all classes, including an improvement of 26% in SN Ia completeness (∼88%) and 2.4% in SN Ia purity (∼95%) when unthresholded (improvements for each class can further be obtained by tuned thresholds), and we discuss the limitation of current SN datasets for benchmarking performance.