Learning-based classification of LAMOST CP1 & CP2 stars

In this study, we present ODCNNnet, a deep learning-based classification model designed to identify CP1 and CP2 stars from low-resolution spectra. The model achieves an accuracy of 99.81% and a recall of 99.43% for CP1 stars, and an accuracy of 98.82% with a recall of 99.41% for CP2 stars.

Applying ODCNNnet to B- to early F-type stars in LAMOST DR11, we identified 33,049 CP1 stars and 5901 CP2 stars, including 5287 newly identified CP1 stars and 1153 newly identified CP2 stars, validated through cross matching with the MKCLASS tool. We further analyzed the stellar parameters of CP stars. The effective temperature (Teff) of CP1 stars ranges from 6000K to 9000K, peaking at 7500K, while CP2 stars span 6000K to 13,000K, with peaks near 8900K and 11,900K.