Published February 25, 2021
| Version v1
Poster
Open
A Deep Learning Approach to photospheric Parameters of CARMENES Target Stars
Creators
- Passegger, Vera Maria1
- Ordieres-Meré, Joaquin2
- Bello-García, Antonio3
- Caballero, José Antonio4
- Schweitzer, Andreas5
- Amado, Pedro J.6
- González-Marcos, Ana7
- Ribas, Ignasi8
- Reiners, Ansgar9
- Quirrenbach, Andreas10
- Sarro, Luis M.11
- Solano, Enrique4
- Azzaro, Marco12
- Bauer, Florian F.6
- Béjar, Victor J. S.13
- Cortés-Contreras, Miriam4
- Dreizler, Stefan9
- Hatzes, Artie P.14
- Henning, Thomas15
- Jeffers, Sandra V.9
- Kaminski, Adrian10
- Kürster, Martin15
- Lafarga, Marina8
- Marfil, Emilio16
- Montes, David16
- Morales, Juan Carlos8
- Nagel, Evangelos17
- Tabernero, Hugo M.18
- Zechmeister, Mathias9
- 1. Homer L. Dodge department of Physics and Astronomy, University of Oklahoma / Hamburger Sternwarte
- 2. Departamento de Ingeniería de Organización, Administración de Empresas y Estadística, Universidad Politécnica de Madrid
- 3. Departamento de Construcción e Ingeniería de Fabricación, Universidad de Oviedo
- 4. Centro de Astrobiología (CSIC-INTA)
- 5. Hamburger Sternwarte
- 6. Instituto de Astrofísica de Andalucía (IAA-CSIC)
- 7. Departamento de Ingeniería Mecánica, Universidad de la Rioja
- 8. Institut de Ciències de l'Espai / Institut d'Estudis Espacials de Catalunya (IEEC)
- 9. Institut für Astrophysik Göttingen
- 10. Landessternwarte Königstuhl
- 11. Departamento de Inteligencia Artificial Madrid
- 12. Centro Astronómico Hispano-Alemán (CSIC-MPG)
- 13. Centro Astronómico Hispano-Alemán (CSIC-MPG) / Instituto de Astrofísica de Canarias
- 14. Thüringer Landessternwarte Tautenburg
- 15. Max-Planck-Institut für Astronomie, Königstuhl
- 16. Departamento de Física de la Tierra y Astrofísica and IPARCOS-UCM
- 17. Thüringer Landessternwarte Tautenburg / Hamburger Sternwarte
- 18. Instituto de Astrofísica e Ciências do Espaço, Universidade do Porto
Contributors
Editor:
Description
We construct an individual convolutional neural network architecture for each of the four stellar parameters effective temperature (Teff), surface gravity (log g), metallicity [M/H], and rotational velocity (v sin i). The networks are trained on synthetic PHOENIX-ACES spectra, showing small training and validation errors. We apply the trained networks to the observed spectra of 283 M dwarfs observed with CARMENES. Although the network models do very well on synthetic spectra, we find large deviations from literature values especially for metallicity, due to the synthetic gap.
Files
Poster_CoolStar_virtualA0.pdf
Files
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Additional details
Related works
- References
- Journal article: 10.1051/0004-6361/202038787 (DOI)
References
- Passegger et al. (2020) arXiv:2008.01186
- Husser et al. (2013) arXiv:1303.5632
- Passegger et al. (2019) arXiv:1907.00807
- Maldonado et al. (2015) arXiv:1503.03010
- Rojas-Ayala et al. (2012) arXiv:1112.4567
- Gaidos & Mann (2014) arXiv:1406.4071
- Mann et al. (2015) arXiv:1501.01635