TY - JOUR
T1 - APOGEE Net
T2 - An Expanded Spectral Model of Both Low-mass and High-mass Stars
AU - Sprague, Dani
AU - Culhane, Connor
AU - Kounkel, Marina
AU - Olney, Richard
AU - Covey, K. R.
AU - Hutchinson, Brian
AU - Lingg, Ryan
AU - Stassun, Keivan G.
AU - Román-Zúñiga, Carlos G.
AU - Roman-Lopes, Alexandre
AU - Nidever, David
AU - Beaton, Rachael L.
AU - Borissova, Jura
AU - Stutz, Amelia
AU - Stringfellow, Guy S.
AU - Ramírez, Karla Peña
AU - Ramírez-Preciado, Valeria
AU - Hernández, Jesús
AU - Kim, Jinyoung Serena
AU - Lane, Richard R.
N1 - Funding Information:
The authors thank the Nvidia Corporation for their donation of GPUs used in this work. K.P.R. acknowledges support from ANID FONDECYT Iniciación 11201161. A.S. gratefully acknowledges funding support through Fondecyt Regular (project code 1180350) and from the ANID BASAL project FB210003. Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions. SDSS acknowledges support and resources from the Center for High-Performance Computing at the University of Utah. The SDSS website is www.sdss.org .
Publisher Copyright:
© 2022. The Author(s). Published by the American Astronomical Society.
PY - 2022/4/1
Y1 - 2022/4/1
N2 - 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.
AB - 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.
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U2 - 10.3847/1538-3881/ac4de7
DO - 10.3847/1538-3881/ac4de7
M3 - Article
AN - SCOPUS:85126665537
VL - 163
JO - Astronomical Journal
JF - Astronomical Journal
SN - 0004-6256
IS - 4
M1 - 152
ER -