A Chemically Peculiar Star Catalog from LAMOST DR12 Based on Deep Learning

Abstract Chemically peculiar (CP) stars offer a unique observational sample for exploring key processes in stellar evolution. Leveraging the massive amounts of data obtained from large-scale sky surveys, deep learning technology has significantly advanced research in identifying special celestial objects in large datasets in recent years. In this study, we developed a hybrid deep learning approach that integrates a long short-term memory network with a convolutional neural network to search for CP1/CP2/CP3 stars in low-resolution spectra of LAMOST DR12 (v1.0). We first trained a binary classification model to distinguish CP stars from non-CP stars, achieving an accuracy of 95.92%. Applying this model to the LAMOST DR12 (v1.0) dataset, we identified 79,216 CP star candidates. We then constructed a three-class classification model to distinguish between CP1, CP2, and CP3, achieving an accuracy rate of 97.34%. This model further identified 59,333 CP1 stars, 15,179 CP2 stars, and 1424 CP3 stars from the CP star candidates. After cross validation, 51,241 stars were consistent with previously reported CP stars based on crossmatching results, with 24,695 newly identified CP1/CP2/CP3 candidates, including 16,432 new CP1 candidates, 7132 CP2 candidates, and 1131 CP3 candidates. By combining visual inspection with the MKCLASS code, we ultimately identified 2899 CP1 stars, 4536 CP2 stars, and 747 CP3 stars.