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Zhenghao Cheng

Publications and source records attributed to Zhenghao Cheng.

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The identification of new Herbig Ae/Be stars from LAMOST DR7

Herbig Ae/Be stars (HAeBes) are critical tracers of intermediate- and high-mass star formation, yet their census remains incomplete compared to low-mass young stellar objects like T-Tauri stars. To expand the known population, we systematically searched for HAeBes in LAMOST DR7 low-resolution spectra. Following Sun et al., we applied Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction and Support Vector Machine (SVM) classification, identifying $\sim$240,000 spectra with potential H$\alpha$ emission. After removing contaminants (non-stellar objects, extragalactic sources, CVs, and Algol systems) and restricting to B/A-type stars, we obtained 1,835 candidates through 2MASS/WISE visual inspection. Spectral energy distribution analysis confirmed 143 sources with infrared excess ($J$-band or longer wavelengths), including 92 known HAeBes. From the remaining 51 candidates, we classified 26 with strong infrared excess as new HAeBes. Color-index analysis of confirmed HAeBes and classical Ae/Be stars (CAeBes) revealed that the $(K-W1)_0$ vs. $(W2-W3)_0$ diagram effectively separates these populations: CAeBes predominantly occupy $(K-W1)_0 \leq 0.5$ and $(W2-W3)_0 \leq 1.1$, while other regions trace transition disks ($(K-W1)_0 < 0.5$ and $(W2-W3)_0 > 1.1$), globally depleted disks ($(K-W1)_0 > 0.5$ and $(W2-W3)_0 < 1.1$), and Class I/Flat/II HAeBes ($(K-W1)_0 > 0.5$ and $(W2-W3)_0 > 1.1$). More importantly, the HAeBes exhibit a clear evolutionary gradient on this diagram, with those in the Class III, Class II, Flat-SED, and Class I evolutionary stages being effectively distinguished by concentric ellipses that are roughly centered at (0,0) with semi-major axes of $a$=1.5, $a$=3.0, and $a$=4.0, and a semi-major to semi-minor axis ratio of 1.6:1.

astro-ph.SR

A catalogue of 323 cataclysmic variables from LAMOST DR6

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$α$-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.

astro-ph.SR