A Deep Learning Approach to photospheric Parameters of CARMENES Target Stars
| Main Authors: | Passegger, Vera Maria, Ordieres-Meré, Joaquin, Bello-García, Antonio, Caballero, José Antonio, Schweitzer, Andreas, Amado, Pedro J., González-Marcos, Ana, Ribas, Ignasi, Reiners, Ansgar, Quirrenbach, Andreas, Sarro, Luis M., Solano, Enrique, Azzaro, Marco, Bauer, Florian F., Béjar, Victor J. S., Cortés-Contreras, Miriam, Dreizler, Stefan, Hatzes, Artie P., Henning, Thomas, Jeffers, Sandra V., Kaminski, Adrian, Kürster, Martin, Lafarga, Marina, Marfil, Emilio, Montes, David, Morales, Juan Carlos, Nagel, Evangelos, Tabernero, Hugo M., Zechmeister, Mathias |
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| Other Authors: | Wolk, Scott |
| Format: | Proceeding poster Journal |
| Terbitan: |
, 2021
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/4562504 |
Daftar Isi:
- 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.