Accurate Teff and [M/H] determinations for the CARMENES M dwarfs from deep transfer learning
Creators
- 1. Departamento de Construcción e Ingeniería de Fabricación, Universidad de Oviedo, c/ Pedro Puig Adam, Sede Departamental Oeste, Módulo 7, 1a planta, E-33203 Gijón, Spain
- 2. Departamento de Ingeniería de Organización, Administración de Empresas y Estadística, Universidad Politécnica de Madrid, c/ José Gutiérrez Abascal 2, E-28006 Madrid, Spain
- 3. Instituto de Astrofísica de Canarias, c/ Vía Láctea s/n, 38205 La Laguna, Tenerife, Spain
- 4. Centro de Astrobiología (CSIC-INTA), ESAC, Camino bajo del castillo s/n, E-28692 Villanueva de la Cañada, Madrid, Spain
- 5. Hamburger Sternwarte, Gojenbergsweg 112, D-21029 Hamburg, Germany
- 6. Departamento de Ingeniería Mecánica, Universidad de la Rioja, c/ San José de Calasanz 31, 26004 Logroño, La Rioja, Spain
Contributors
Description
Accurate Teff and [M/H] determinations for the CARMENES M dwarfs from deep transfer learning
We present a new technique for deriving photospheric stellar parameters of M dwarfs with high accuracy,
the so-called transfer learning. There, information from one domain is being transferred to improve the
accuracy of a neural network model in another domain.
We demonstrate the feasibility of the deep transfer learning approach applied to M-dwarf high-resolution
spectra from the CARMENES survey. In order to do so, we use 14 stars of the CARMENES sample with interferometric
angular diameters to calculate the effective temperature, as well as six M dwarfs being common proper-motion
companions to FGK-type primaries with known metallicity.
After we train a deep learning neural network model on synthetic PHOENIX-ACES spectra, we use the internal
feature representations together with those 14+6 independently labeled stars as a new input for the transfer process.
We compare the derived stellar parameters of a small sample of M dwarfs kept out of the training phase
with results from other methods. We demonstrate that deep transfer learning provides a higher accuracy
than our previous deep learning method, indicating that it is a robust tool for obtaining M-dwarf
stellar parameters when measured against samples from independent estimations for well known stars.
Files
CS21-ML.DTL.V7.pdf
Files
(987.8 kB)
| Name | Size | Download all |
|---|---|---|
|
md5:95ed6b3cd3a15c9e4c819a27afc772ed
|
987.8 kB | Preview Download |
Additional details
References
- Boyajian T. S., McAlister H. A., Baines E. K. et al. 2008 ApJ 683 424
- Boyajian, T. S., von Braun, K., van Belle, G., et al. 2012, ApJ, 757, 112
- Bressan, A., Marigo, P., Girardi, L., et al. 2012, MNRAS, 427, 127
- Husser, T.-O., Wende-von Berg, S., Dreizler, S., et al. 2013, A&A, 553, A6
- Marfil, E., Tabernero, H. M., Montes, D., et al. 2021, A&A, 656, A162
- Montes, D., González-Peinado, R., Tabernero, H. M., et al. 2018, MNRAS, 479, 1332
- Passegger, V. M., Bello-García, A., Ordieres-Meré, J., et al. 2020, A&A, 642, A22
- Passegger, V. M., Bello-García, A., Ordieres-Meré, J., et al. 2022, A&A, 658, A194
- von Braun, K., Boyajian, T. S., van Belle, G. T., et al. 2014, MNRAS, 438, 2413