Using autoencoders and deep transfer learning to determine the stellar parameters of 286 CARMENES M dwarfs^★

¹ Centro de Astrobiología (CAB), CSIC-INTA, Camino Bajo del Castillo s/n, 28692 Villanueva de la Canada, Madrid, Spain
e-mail: pmas@cab.inta-csic.es
² Departamento de Ingeniería Mecánica, Universidad de la Rioja, San José de Calasanz 31, 26004 Logroño, La Rioja, Spain
³ Instituto de Astrofísica de Canarias, c/ Via Láctea s/n, 38205 La Laguna, Tenerife, Spain
⁴ Departamento de Astrofísica, Universidad de La Laguna, 38206 La Laguna, Tenerife, Spain
⁵ Hamburger Sternwarte, Gojenbergsweg 112, 21029 Hamburg, Germany
⁶ Departamento de Física de la Tierra y Astrofísica & IPARCOS-UCM (Instituto de Física de Partículas y del Cosmos de la UCM), Facultad de Ciencias Físicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
⁷ Departamento de Ingeniería de Organization, Administración de Empresas y Estadística, Universidad Politécnica de Madrid, c/ José Gutiérrez Abascal 2, 28006 Madrid, Spain
⁸ Departamento de Construcción e Ingeniería de Fabricación, Universidad de Oviedo, Pedro Puig Adam, Sede Departamental Oeste, Módulo 7, 1 a planta, 33203 Gijón, Spain

Received: 5 March 2024
Accepted: 2 May 2024

Abstract

Context. Deep learning (DL) techniques are a promising approach among the set of methods used in the ever-challenging determination of stellar parameters in M dwarfs. In this context, transfer learning could play an important role in mitigating uncertainties in the results due to the synthetic gap (i.e. difference in feature distributions between observed and synthetic data).

Aims. We propose a feature-based deep transfer learning (DTL) approach based on autoencoders to determine stellar parameters from high-resolution spectra. Using this methodology, we provide new estimations for the effective temperature, surface gravity, metallicity, and projected rotational velocity for 286 M dwarfs observed by the CARMENES survey.

Methods. Using autoencoder architectures, we projected synthetic PHOENIX-ACES spectra and observed CARMENES spectra onto a new feature space of lower dimensionality in which the differences between the two domains are reduced. We used this low-dimensional new feature space as input for a convolutional neural network to obtain the stellar parameter determinations.

Results. We performed an extensive analysis of our estimated stellar parameters, ranging from 3050 to 4300 K, 4.7 to 5.1 dex, and −0.53 to 0.25 dex for T_eff, log 𝑔, and [Fe/H], respectively. Our results are broadly consistent with those of recent studies using CARMENES data, with a systematic deviation in our T_eff scale towards hotter values for estimations above 3750 K. Furthermore, our methodology mitigates the deviations in metallicity found in previous DL techniques due to the synthetic gap.

Conclusions. We consolidated a DTL-based methodology to determine stellar parameters in M dwarfs from synthetic spectra, with no need for high-quality measurements involved in the knowledge transfer. These results suggest the great potential of DTL to mitigate the differences in feature distributions between the observations and the PHOENIX-ACES spectra.