Carbon stars identified from LAMOST DR4 using Machine Learning

TL;DR

This paper presents a catalog of 2651 carbon stars identified from LAMOST DR4 spectra using machine learning, including subtype classification, photometric matching, and potential binary system candidates.

Contribution

The study introduces an efficient machine learning approach to identify and classify a large sample of carbon stars from spectroscopic data, including new subtype distinctions and binary system indicators.

Findings

Catalog of 2651 carbon stars from LAMOST DR4.

Identification of 17 carbon-enhanced metal-poor (CEMP) candidates.

Detection of 25 carbon stars with ultraviolet signatures suggesting binary companions.

Abstract

In this work, we present a catalog of 2651 carbon stars from the fourth Data Release (DR4) of the Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST). Using an efficient machine-learning algorithm, we find out these stars from more than seven million spectra. As a by-product, 17 carbon-enhanced metal-poor (CEMP) turnoff star candidates are also reported in this paper, and they are preliminarily identified by their atmospheric parameters. Except for 176 stars that could not be given spectral types, we classify the other 2475 carbon stars into five subtypes including 864 C-H, 226 C-R, 400 C-J, 266 C-N, and 719 barium stars based on a series of spectral features. Furthermore, we divide the C-J stars into three subtypes of CJ( H), C-J(R), C-J(N), and about 90% of them are cool N-type stars as expected from previous literature. Beside spectroscopic classification, we also match these carbon stars to multiple broadband photometries. Using ultraviolet photometry data, we find that 25 carbon stars have FUV detections and they are likely to be in binary systems with compact white dwarf companions.