Hunan Provincial Key Laboratory of Stellar & Interstellar Medium · Lei Lab, Xiangtan University
Hot subdwarfs are rare among survey spectra (a few per ten thousand); manual vetting cannot cover the tens of millions of LAMOST / Gaia spectra. How do we find them reliably at scale?
Most hot subdwarfs form through binary interactions, but the relative contribution of each channel — and how extreme systems (e.g. highly eccentric wide binaries) survive — remain open.
Classical spectral fitting needs manual initial guesses and per-target computation, which cannot scale to surveys. Can models learn to "read" spectra and return atmospheric parameters in milliseconds?
Blue hook stars in globular clusters and the bimodality of horizontal-branch morphology resist a single stellar-evolution picture; how do binary fractions and winds shape the observed populations?
Deep learning predicts atmospheric parameters straight from spectra with classical-fitting-level accuracy, orders of magnitude faster — the technical foundation of this site's services.
Identifies 222 hot subdwarfs in LAMOST DR8 (131 composite) and reports the most eccentric wide hot-subdwarf binary known, challenging formation-channel models.
Proposes the tidally enhanced stellar-wind channel for blue hook stars in globular clusters — a representative theoretical work.
Enter Teff, log g and helium abundance; get a theoretical spectrum in milliseconds.
Upload an observed spectrum; we sample posteriors over an NLTE model grid via χ² MCMC.