A catalogue of 323 cataclysmic variables from LAMOST DR6

Abstract

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 H$\alpha$-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.

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• Searching for cataclysmic variable stars in unidentified X-ray sources
• A persistent bow shock in a diskless magnetised accreting white dwarf
• The identification of new Herbig Ae/Be stars from LAMOST DR7
• LAMOST J040901.83+329355.6 -- a new Galactic star with Wolf--Rayet characteristics on a post-AGB to CSPN transitional stage
• Full-frame data reduction method: a data mining tool to detect the potential variations in optical photometry