8.6k citations · 12.2k across the 44 of their papers we have counts for
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Disentangling stellar atmospheric parameters in astronomical spectra using Generative Adversarial Neural Networks
Minia Manteiga, Raúl Santoveña, Marco A. Álvarez +4
A method based on Generative Adversaria! Networks (GANs) is developed for disentangling the physical (effective temperature and gravity) and chemical (metallicity, overabundance of…
A method based on Generative Adversarial Networks for disentangling physical and chemical properties of stars in astronomical spectra
Raúl Santoveña, Carlos Dafonte, Minia Manteiga
Data compression techniques focused on information preservation have become essential in the modern era of big data. In this work, an encoder-decoder architecture has been designed…
Gaia Data Release 2. Short-timescale variability processing and analysis
M. Roelens, L. Eyer, N. Mowlavi +13
The Gaia DR2 sample of short-timescale variable candidates results from the investigation of the first 22 months of Gaia photometry for a subsample of sources at the Gaia faint end…
Gaia Data Release 2: Properties and validation of the radial velocities
D. Katz, P. Sartoretti, M. Cropper +51
For Gaia DR2 (GDR2), 280 million spectra, collected by the RVS instrument on-board Gaia, were processed and median radial velocities were derived for 9.8 million sources brighter t…
Gaia Data Release 2: Photometric content and validation
D. W. Evans, M. Riello, F. De Angeli +9
Aims. We describe the photometric content of the second data release of the Gaia project (Gaia DR2) and its validation along with the quality of the data. Methods. The validation w…
On the estimation of stellar parameters with uncertainty prediction from Generative Artificial Neural Networks: application to Gaia RVS simulated spectra
C. Dafonte, D. Fustes, M. Manteiga +4
Aims. We present an innovative artificial neural network (ANN) architecture, called Generative ANN (GANN), that computes the forward model, that is it learns the function that rela…