TY  - JOUR
AU  - de Diego, José A.
AU  - Nadolny, Jakub
AU  - Bongiovanni, Ángel
AU  - Cepa, Jordi
AU  - Povic, Mirjana
AU  - Pérez García, Ana María
AU  - Padilla Torres, Carmen P.
AU  - Lara-López, Maritza A.
AU  - Cerviño, Miguel
AU  - Pérez Martínez, Ricardo
AU  - Alfaro, Emilio J.
AU  - Castañeda, Héctor O.
AU  - Fernández-Lorenzo, Miriam
AU  - Gallego Maestro, Jesús
AU  - González, J. Jesús
AU  - González-Serrano, J. Ignacio
AU  - Pintos-Castro, Irene
AU  - Sánchez-Portal, Miguel
AU  - Cedrés, Bernabé
AU  - González-Otero, Mauro
AU  - Jones, D. Heath
AU  - Bland-Hawthorn, Joss
PY  - 2020
DO  - 10.1051/0004-6361/202037697
SN  - 0004-6361
UR  - https://hdl.handle.net/20.500.14352/6478
T2  - Astronomy & Astrophysics
AB  - Context. The accurate classification of hundreds of thousands of galaxies observed in modern deep surveys is imperative if we want to understand the universe and its evolution.Aims. Here, we report the use of machine learning techniques to classify...
LA  - eng
PB  - EDP Sciencies
KW  - Convolutional neuronal-networks
KW  - Star-formation histories
KW  - Evolution survey cosmos
KW  - Morphological classification
KW  - Fundamental properties
KW  - Photometric redshifts
KW  - Stellar masses
KW  - Missing values
KW  - Disk
KW  - Decomposition
TI  - Galaxy classification: deep learning on the OTELO and COSMOS databases
TY  - journal article
VL  - 638
ER  -