A hybrid SLAM-Payne framework for atmospheric parameter and abundance determination of early-type stars from LAMOST DR9 low-resolution spectra

Context. Early-type stars are key drivers of Galactic chemical evolution, enriching the interstellar medium with alpha elements through powerful stellar winds and core-collapse supernovae, fuelled by their short lifetimes and high masses. However, their spectra remain challenging to analyse due to rapid rotation, weak metal lines, and non-local thermodynamic equilibrium (NLTE) effects. While large spectroscopic surveys provide extensive low-resolution data, extracting reliable parameters remains difficult due to methodological limitations for hot stars. Aims. Our goal is to develop a unified framework that combines data-driven and synthetic spectral approaches to determine atmospheric parameters and abundances for hot stars using low-resolution spectra, thereby addressing limitations in current methodologies while retaining critical spectral information. Methods. We present a hybrid approach integrating the Stellar LAbel Machine (SLAM) and the Payne frameworks, for low-resolution (R~1800) spectra from LAMOST DR9. Our method preserves full spectral information including Balmer series and metal-line blends, employing neural-network interpolation for efficient parameter estimation (Teff, log g, and v sin i) and abundance determination for O, Mg, Si, and Fe, across 8000 K–20 000 K. Results. We derive stellar parameters and abundances for 315 822 stars with S/N ⩾ 10 in the r-band. Among these, we identify 3564 blue horizontal branch candidates, over 90% of which align with stellar evolutionary models of horizontal branch stars. Additionally, we detect abundance trends ([α/Fe]–[Fe/H]) that exhibit temperature-dependent systematics and a distinct α-poor stellar population within 0.0 ⩽ [Fe/H] ⩽ 0.5 dex. The radial abundance gradients are negative and consistent with that derived from Cepheids, with a slope of −0.070 ± 0.007 in [Fe/H] in the region 6 ⩽ RGC ⩽ 15 kpc.