Abstract The Sloan Digital Sky Survey-V (SDSS-V) Magellanic Genesis Survey is a spectroscopic program designed to map the kinematic and chemical structure of the Magellanic Clouds (MCs) using APOGEE and BOSS spectroscopy. This overview describes the survey’s design, target selection, and science goals and highlights some first results using these data. In the inner regions of the Large and Small Magellanic Clouds (LMC and SMC), the survey obtained high-resolution near-infrared APOGEE spectra (S/N ≈ 45) of ∼14,000 bright, oxygen-rich asymptotic giant branch (AGB-O) stars. These data provide contiguous spatial coverage of the MCs’ main bodies, enabling detailed chemodynamical studies. To explore extended structures, the survey includes BOSS optical spectroscopy of fainter red giant stars selected with Gaia DR3 data, reachingG ≈ 17.5. Many of these targets extend to the outer regions of the MCs, which are known to span ∼20° (LMC) and ∼12° (SMC) and contain diffuse substructures of unclear origin. BOSS data in the inner regions also complement APOGEE by providing elements inaccessible in the near-infrared and enabling cross-calibration between instruments. The survey further includes APOGEE and BOSS observations of ∼300 evolved massive stars and a small sample of symbiotic binaries previously observed by APOGEE-1 and APOGEE-2, enhancing our understanding of massive stellar evolution and complementing the SDSS-V main-sequence massive star program.
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Zeta-Payne: A Fully Automated Spectrum Analysis Algorithm for the Milky Way Mapper Program of the SDSS-V Survey
Abstract 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, <0.1 dex (<0.05 dex) for log g , and 0.4–0.5 dex (0.1 dex) for [M/H] of the star, respectively. We find a good agreement between atmospheric parameters of OBAF-type stars when inferred from their high- and low-resolution optical spectra. For most stellar labels, the APOGEE spectra are (far) less informative than the BOSS spectra of these stars, while log g , v sin i , and [M/H] are in most cases too uncertain for meaningful astrophysical interpretation. This makes BOSS low-resolution optical spectra better for stellar labels of OBAF-type stars, unless the latter are subject to high levels of extinction.
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- Award ID(s):
- 2034429
- PAR ID:
- 10465594
- Date Published:
- Journal Name:
- The Astronomical Journal
- Volume:
- 163
- Issue:
- 5
- ISSN:
- 0004-6256
- Page Range / eLocation ID:
- 236
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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