A Deep Learning Approach to photospheric Parameters of CARMENES Target Stars

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 et al.
Bibliographical reference

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

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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.