Objective Separation between CP1 and CP2 Based on Feature Extraction with Machine Learning

Abstract

In the eighth data release (DR8) of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, more than 318,740 low-resolution stellar spectra with types from B to early F and signal-to-noise ratios >50 were released. With this large volume of the early-type stars, we tried machine-learning algorithms to search for class-one and class-two chemical peculiars (CP1 and CP2), and to detect spectral features to distinguish the two classes in low-resolution spectra. We selected the XGBoost algorithm after comparing the classification efficiency of three machine-learning ensemble algorithms. Using XGBoost followed by the visual investigation, we presented a catalog of 20,694 sources, including 17,986 CP1 and 2708 CP2, in which 6917 CP1 and 1652 CP2 are newly discovered. We also list the spectral features to separate CP1 from CP2 discovered through XGBoost. The stellar parameters (including effective temperature (T eff), surface gravity (log g), metallicity [Fe/H]), the spatial distribution in Galactic coordinates, and the color magnitude were provided for all of the entries of the catalog. The T eff for CP1 distributes from ∼6000 to ∼8500 K, while for CP2 it distributes from ∼7000 to ∼13,700 K. The log g of CP1 ranges from 2.8 to 4.8 dex, peaking at 4.5 dex, and of CP2 it ranges from 2.0 to 5.0 dex, peaking at 3.6 dex, respectively. The [Fe/H] of CP1 and CP2 are from −1.4 to 0.4 dex, and the [Fe/H] of CP1 are on average higher than that of CP2. Almost all of the targets in our sample locate around the Galactic plane.