Proyecto de Investigación: RADIO CUASARES OSCURECIDOS DESVELADOS
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ID
PGC2018-099705-B-I00
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Galaxy classification: Deep learning on the OTELO and COSMOS databases
(EDP Sciences, 2020-06-25) De Diego, J. A.; Nadolny, J.; Bongiovanni, Á.; Cepa, J.; Povic, M.; Pérez García, A. M.; Padilla Torres, Carmen P.; Lara López, M. A.; Cerviño, M.; Pérez Martínez, R.; Alfaro, Emilio J.; Castañeda, H. O.; Fernández Lorenzo, M.; Gallego, J.; González, J. J.; González Serrano, J. I.; Pintos Castro, I.; Sánchez Portal, M.; Cedrés, B.; González Otero, M.; Jones, D. Heath; Bland Hawthorn, J.; Ministerio de Economía y Competitividad (MINECO); Agencia Estatal de Investigación (AEI); De Diego, J. A. https://orcid.org/0000-0001-7040-069X; Centros de Excelencia Severo Ochoa, INSTITUTO DE ASTROFISICA DE ANDALUCIA (IAA), SEV-2017-0709; Unidad de Excelencia Científica María de Maeztu Centro de Astrobiología del Instituto Nacional de Técnica Aeroespacial y CSIC, MDM-2017-0737
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 Sersic 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.
