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

Dani Sprague, Connor Culhane, Marina Kounkel, Richard Olney, K. R. Covey, Brian Hutchinson, Ryan Lingg, Keivan G. Stassun, Carlos G. Román-Zúñiga, Alexandre Roman-Lopes, David Nidever, Rachael L. Beaton, Jura Borissova, Amelia Stutz, Guy S. Stringfellow, Karla Peña Ramírez, Valeria Ramírez-Preciado, Jesús Hernández, Jinyoung Serena Kim, Richard R. Lane

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number152
JournalAstronomical Journal
Volume163
Issue number4
DOIs
StatePublished - Apr 1 2022
Externally publishedYes

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science

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