Deep Learning-based Identification of CP1 and CP2 Stars from LAMOST DR11

Abstract Chemically peculiar (CP) stars, particularly CP1 (metallic-line) and CP2 (magnetic Ap/Bp) stars, are crucial for studying microscopic processes in stellar atmospheres, such as atomic diffusion and magnetic interactions. 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 ( T eff ) of CP1 stars ranges from 6000 K to 9000 K, peaking at 7500 K, while CP2 stars span 6000 K to 13,000 K, with peaks near 8900 K and 11,900 K. The surface gravity ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>log</mml:mi> </mml:math> g ) of CP1 stars is concentrated between 3.4 and 4.6 dex, peaking at 3.9 dex, while CP2 stars range from 3.5 to 4.6 dex. The [Fe/H] of CP1 stars is generally between −1.0 and 0.8 dex, peaking around −0.1 dex, whereas CP2 stars exhibit a broader distribution from −1.5 dex to 1.0 dex, with peaks around −0.25 dex and 0.75 dex. This study provides a valuable data set for investigating the physical properties of CP stars and their underlying formation mechanisms.