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

doi:10.3847/1538-3881/ac4de7

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

Steward Observatory

Research output: Contribution to journal › Article › peer-review

26 Scopus citations

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 language	English (US)
Article number	152
Journal	Astronomical Journal
Volume	163
Issue number	4
DOIs	https://doi.org/10.3847/1538-3881/ac4de7
State	Published - Apr 1 2022

ASJC Scopus subject areas

Astronomy and Astrophysics
Space and Planetary Science

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10.3847/1538-3881/ac4de7

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Sprague, D., Culhane, C., Kounkel, M., Olney, R., Covey, K. R., Hutchinson, B., Lingg, R., Stassun, K. G., Román-Zúñiga, C. G., Roman-Lopes, A., Nidever, D., Beaton, R. L., Borissova, J., Stutz, A., Stringfellow, G. S., Ramírez, K. P., Ramírez-Preciado, V., Hernández, J., Kim, J. S., & Lane, R. R. (2022). APOGEE Net: An Expanded Spectral Model of Both Low-mass and High-mass Stars. Astronomical Journal, 163(4), Article 152. https://doi.org/10.3847/1538-3881/ac4de7

Sprague, D, Culhane, C, Kounkel, M, Olney, R, Covey, KR, Hutchinson, B, Lingg, R, Stassun, KG, Román-Zúñiga, CG, Roman-Lopes, A, Nidever, D, Beaton, RL, Borissova, J, Stutz, A, Stringfellow, GS, Ramírez, KP, Ramírez-Preciado, V, Hernández, J, Kim, JS & Lane, RR 2022, 'APOGEE Net: An Expanded Spectral Model of Both Low-mass and High-mass Stars', Astronomical Journal, vol. 163, no. 4, 152. https://doi.org/10.3847/1538-3881/ac4de7

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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.",

author = "Dani Sprague and Connor Culhane and Marina Kounkel and Richard Olney and Covey, {K. R.} and Brian Hutchinson and Ryan Lingg and Stassun, {Keivan G.} and Rom{\'a}n-Z{\'u}{\~n}iga, {Carlos G.} and Alexandre Roman-Lopes and David Nidever and Beaton, {Rachael L.} and Jura Borissova and Amelia Stutz and Stringfellow, {Guy S.} and Ram{\'i}rez, {Karla Pe{\~n}a} and Valeria Ram{\'i}rez-Preciado and Jes{\'u}s Hern{\'a}ndez and Kim, {Jinyoung Serena} and Lane, {Richard R.}",

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doi = "10.3847/1538-3881/ac4de7",

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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.

PY - 2022/4/1

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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.

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

Abstract

ASJC Scopus subject areas

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Stellar parameters for ~650000 stars with APOGEE

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

Abstract

ASJC Scopus subject areas

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Datasets

Stellar parameters for ~650000 stars with APOGEE

Cite this