Zeta-Payne: A Fully Automated Spectrum Analysis Algorithm for the Milky Way Mapper Program of the SDSS-V Survey

The Sloan Digital Sky Survey (SDSS) has recently initiated its fifth survey generation (SDSS-V), with a central focus on stellar spectroscopy. In particular, SDSS-V's Milky Way Mapper program will deliver multiepoch optical and near-infrared spectra for more than 5 × 10 6 stars across the entire sky, covering a large range in stellar mass, surface temperature, evolutionary stage, and age. About 10% of those spectra will be of hot stars of OBAF spectral types, for whose analysis no established survey pipelines exist. Here we present the spectral analysis algorithm, ZETA-PAYNE, developed specifically to obtain stellar labels from SDSS-V spectra of stars with these spectral types and drawing on machine-learning tools. We provide details of the algorithm training, its test on artificial spectra, and its validation on two control samples of real stars. Analysis with ZETA-PAYNE leads to only modest internal uncertainties in the near-IR with APOGEE (optical with BOSS): 3%–10% (1%–2%) for T eff , 5%–30% (5%–25%) for v sin i , 1.7–6.3 km s −1 (0.7–2.2 km s −1 ) for radial velocity,