In this work, we present a catalog of cataclysmic variables (CVs) identified from the Sixth Data Release (DR6) of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). To single out the CV spectra, we introduce a novel machine-learning algorithm called UMAP to screen out a total of 169,509 Hα-emission spectra, and obtain a classification accuracy of the algorithm of over 99.6% from the cross-validation set. We then apply the template matching program PyHammer v2.0 to the LAMOST spectra to obtain the optimal spectral type with metallicity, which helps us identify the chromospherically active stars and potential binary stars from the 169,509 spectra. After visually inspecting all the spectra, we identify 323 CV candidates from the LAMOST database, among them 52 objects are new. We further discuss the new CV candidates in subtypes based on their spectral features, including five DN subtype during outbursts, five NL subtype and four magnetic CVs (three AM Her type and one IP type). We also find two CVs that have been previously identified by photometry, and confirm their previous classification by the LAMOST spectra.
@article{arxiv.2111.13049,
title = {A catalogue of 323 cataclysmic variables from LAMOST DR6},
author = {Yongkang Sun and Zhenghao Cheng and Shuo Ye and Ruobin Ding and Yijiang Peng and Jiawen Zhang and Zhenyan Huo and Wenyuan Cui and Xiaofeng Wang and Jianrong Shi and Jie Lin and Chengyuan Wu and Linlin Li and Shuai Feng and Yang Yu and Xiaoran Ma and Xin Li and Cheng Liu and Ziping Zhang and Zhenzhen Shao},
journal= {arXiv preprint arXiv:2111.13049},
year = {2021}
}