A catalogue of 323 cataclysmic variables from LAMOST DR6

TL;DR

This paper presents a catalog of 323 cataclysmic variables identified from LAMOST DR6 spectra, using machine learning and spectral analysis, including 52 new discoveries and subtype classifications.

Contribution

Introduces a novel machine-learning approach using UMAP for efficient CV identification from large spectral datasets, and provides a comprehensive catalog with new and confirmed CVs.

Findings

Identified 323 CV candidates from LAMOST DR6, including 52 new objects.

Achieved over 99.6% classification accuracy with the UMAP algorithm.

Classified CVs into subtypes based on spectral features, including DNe, NL, and magnetic CVs.

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.