537 citations · 5.1k across the 108 of their papers we have counts for
46 papers · 1 filter
Astro+ database. I. Description and first results
K. Rübke, A. Marco, I. Negueruela +4
The vast amounts of spectroscopic data for massive stars provided by previous and existing instruments on ground-based and space-based telescopes have saturated our capability to p…
The CARMENES search for exoplanets around M dwarfs. A homogeneous catalogue of projected rotational velocities accounting for limb-darkening
R. Varas, G. Morello, M. Zechmeister +18
Stellar rotation is closely linked to both age and magnetic activity. Through gyrochronology, it provides a means to estimate stellar ages and trace the evolution of planetary syst…
A neural network approach to determining photometric metallicities of M-type dwarf stars
C. Duque-Arribas, H. M. Tabernero, D. Montes +2
M dwarfs are the most abundant stars in the Galaxy and serve as key targets for stellar and exoplanetary studies. It is particularly challenging to determine their metallicities be…
CARMENES input catalogue of M dwarfs VIII. Kinematics in the solar neighbourhood
M. Cortés-Contreras, J. A. Caballero, D. Montes +15
Aims. Our goals are to characterise the kinematic properties and to identify young and old stars among the M dwarfs of the CARMENES input catalogue. Methods. We compiled the spectr…
The CARMENES search for exoplanets around M dwarfs: Magnesium and silicon abundances of K7-M5.5 stars
H. M. Tabernero, Y. Shan, J. A. Caballero +13
We present the abundances of magnesium (Mg) and silicon (Si) for 314 dwarf stars with spectral types in the interval K7.0-M5.5 (Teff range ~4200-3050 K) observed with the CARMENES…
Using autoencoders and deep transfer learning to determine the stellar parameters of 286 CARMENES M dwarfs
P. Mas-Buitrago, A. González-Marcos, E. Solano +9
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, trans…