2023/11/29 by Alexander Stone-Martinez, Stone-Martinez, Alexander, Jon A. Holtzman +9 · 4 citations
Engineering · Physics and Astronomy · #Astronomical Observations and Instrumentation #Astronomy and Astrophysical Research #Astrophysics of Galaxies (astro-ph.GA) #FOS: Physical sciences #Solar and Stellar Astrophysics (astro-ph.SR) #Stellar, planetary, and galactic studies
paper · doi:10.48550/arxiv.2311.17887
openalex publication_date 2023/11/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We derive distances and masses of stars from the Sloan Digital Sky Survey (SDSS) Apache Point Observatory Galactic Evolution Experiment (APOGEE) Data Release 17 (DR17) using simple neural networks. Training data for distances comes from Gaia EDR3, supplemented by literature distances for star clusters. For masses, the network is trained using asteroseismic masses for evolved stars and isochrone masses for main sequence stars. The models are trained on effective temperature, surface gravity, metallicity and carbon and nitrogen abundances. We found that our distance predictions have median fractional errors that range from ≈ 20% at low log g and ≈ 10% at higher log g with a standard deviation of ≈ 11%. The mass predictions have a standard deviation of ± 12%. Using the masses, we derive ages for evolved stars based on the correspondence between mass and age for giant stars given by isochrones. The results are compiled into a Value Added Catalog (VAC) called DistMass that contains distances and masses for 733901 independent spectra, plus ages for 396548 evolved stars.