2026-06-27T10:59:26Zhttps://docta.ucm.es/rest/oai/request
oai:docta.ucm.es:20.500.14352/64782023-08-25T13:14:26Zcom_20.500.14352_14col_20.500.14352_15
Docta Complutense author de Diego, José A. author Nadolny, Jakub author Bongiovanni, Ángel author Cepa, Jordi author Povic, Mirjana author Pérez García, Ana María author Padilla Torres, Carmen P. author Lara-López, Maritza A. author Cerviño, Miguel author Pérez Martínez, Ricardo author Alfaro, Emilio J. author Castañeda, Héctor O. author Fernández-Lorenzo, Miriam author Gallego Maestro, Jesús author González, J. Jesús author González-Serrano, J. Ignacio author Pintos-Castro, Irene author Sánchez-Portal, Miguel author Cedrés, Bernabé author González-Otero, Mauro author Jones, D. Heath author Bland-Hawthorn, Joss 2023-06-16T15:21:40Z 2023-06-16T15:21:40Z 2020-06-25 0004-6361 10.1051/0004-6361/202037697 https://hdl.handle.net/20.500.14352/6478 http://dx.doi.org/10.1051/0004-6361/202037697 https://www.aanda.org/ 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 early- and late-type galaxies in the OTELO and COSMOS databases using optical and infrared photometry and available shape parameters: either the Sérsic index or the concentration index. Methods. We used three classification methods for the OTELO database: (1) u − r color separation, (2) linear discriminant analysis using u − r and a shape parameter classification, and (3) a deep neural network using the r magnitude, several colors, and a shape parameter. We analyzed the performance of each method by sample bootstrapping and tested the performance of our neural network architecture using COSMOS data. Results. The accuracy achieved by the deep neural network is greater than that of the other classification methods, and it can also operate with missing data. Our neural network architecture is able to classify both OTELO and COSMOS datasets regardless of small differences in the photometric bands used in each catalog. Conclusions. In this study we show that the use of deep neural networks is a robust method to mine the cataloged data. eng Galaxy classification: deep learning on the OTELO and COSMOS databases journal article URL https://docta.ucm.es/bitstreams/d06e0b32-7a5d-42f6-aaec-8be133c3d9f6/download File MD5 dca21bbeb3f61c8e6bb511153b8b5489 1629785 application/pdf jesusgallego149libre.pdf URL https://docta.ucm.es/bitstreams/6d2e47f9-ac91-40e2-b164-a1b9524df966/download File MD5 1e54f4cb913317dc7d0eed71ccd33aeb 143183 text/plain jesusgallego149libre.pdf.txt