APOGEE Net: An Expanded Spectral Model of Both Low-mass and High-mass Stars

We train a convolutional neural network, APOGEE Net, to predict T eff, logg, and, for some stars, [Fe/H], based on the APOGEE spectra. This is the first pipeline adapted for these data that is capable of estimating these parameters in a self-consistent manner not only for low-mass stars, (such as main-sequence dwarfs, pre-main-sequence stars, and red giants), but also high-mass stars with T eff in excess of 50,000 K, including hot dwarfs and blue supergiants. The catalog of ∼650,000 stars presented in this paper allows for a detailed investigation of the star-forming history of not just the Milky Way, but also of the Magellanic clouds, as different type of objects tracing different parts of these galaxies can be more cleanly selected through their distinct placement in T eff-logg parameter space than in previous APOGEE catalogs produced through different pipelines. © 2022. The Author(s). Published by the American Astronomical Society.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]