A Deep Learning Approach to photospheric Parameters of CARMENES Target Stars

Bibcode
2021csss.confE..71P
DOI
10.5281/zenodo.4562504
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
Bibliographical reference

Cambridge Workshop on Cool Stars, Stellar Systems, and the Sun

Advertised on:
3
2021
Number of authors
29
IAC number of authors
1
Citations
1
Refereed citations
1
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.
Type
Non-refereed

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