Identification of New Be Stars from LAMOST Data Release 11 Based on Deep Learning

Abstract Be stars are rapidly rotating B-type stars that exhibit Balmer emission lines in their optical spectra, which makes them crucial for studying stellar evolution and circumstellar disk structures. In this study, we performed a systematic identification of Be stars using low-resolution spectroscopic data from Large Sky Area Multi-Object Fiber Spectroscopic Telescope Data Release 11. We constructed a dataset and developed a hybrid identification model combining long short-term memory networks and convolutional neural networks, achieving a testing accuracy of 97.83%. The trained model was applied to spectra with signal-to-noise ratios greater than 10 in the g band, yielding 55,667 B-type candidates. After further validation using the MKCLASS tool, manual verification, and removing duplicated observational data, 26,925 B-type candidates were confirmed. These B-type candidates were subsequently crossmatched with published H α emission-line catalogs to identify emission-line B-type objects, yielding a sample of 5384 Be star candidates. By comparing these samples with existing Be star databases, we identified 2881 previously reported objects and 2503 newly discovered Be stars. Based on infrared color criteria, 1658 of these new detections were classified as classical Be stars and 32 as Herbig Be stars. The remaining 813 objects were categorized as inconclusive, as they either exhibited ambiguous classifications satisfying only partial infrared criteria or lacked sufficient photometric information for definitive categorization.