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5 papers · 1 filter
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…
J-PLUS: Discovery and characterisation of ultracool dwarfs using Virtual Observatory tools II. Second data release and machine learning methodology
P. Mas-Buitrago, E. Solano, A. González-Marcos +20
Ultracool dwarfs (UCDs) comprise the lowest mass members of the stellar population and brown dwarfs, from M7 V to cooler objects with L, T, and Y spectral types. Most of them have…
The CARMENES search for exoplanets around M dwarfs -- A deep learning approach to determine fundamental parameters of target stars
V. M. Passegger, A. Bello-García, J. Ordieres-Meré +26
Existing and upcoming instrumentation is collecting large amounts of astrophysical data, which require efficient and fast analysis techniques. We present a deep neural network arch…
Gaia Data Release 1. Testing the parallaxes with local Cepheids and RR Lyrae stars
Gaia Collaboration, G. Clementini, L. Eyer +588
Parallaxes for 331 classical Cepheids, 31 Type II Cepheids and 364 RR Lyrae stars in common between Gaia and the Hipparcos and Tycho-2 catalogues are published in Gaia Data Release…
Gaia Data Release 1. Open cluster astrometry: performance, limitations, and future prospects
Gaia Collaboration, F. van Leeuwen, A. Vallenari +589
Context. The first Gaia Data Release contains the Tycho-Gaia Astrometric Solution (TGAS). This is a subset of about 2 million stars for which, besides the position and photometry,…