Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12666/128
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dc.rights.license© ESO 2020-
dc.contributor.authorDe Diego, J. A.-
dc.contributor.authorNadolny, J.-
dc.contributor.authorBongiovanni, Á.-
dc.contributor.authorCepa, J.-
dc.contributor.authorPovic, M.-
dc.contributor.authorPérez García, A. M.-
dc.contributor.authorPadilla Torres, C. P.-
dc.contributor.authorLara López, M. A.-
dc.contributor.authorCerviño, M.-
dc.contributor.authorPérez Martínez, R.-
dc.contributor.authorAlfaro, Emilio J.-
dc.contributor.authorCastañeda, H. O.-
dc.contributor.authorFernández Lorenzo, M.-
dc.contributor.authorGallego, J.-
dc.contributor.authorGonzález, J. J.-
dc.contributor.authorGonzález Serrano, J. I.-
dc.contributor.authorPintos Castro, I.-
dc.contributor.authorSánchez Portal, M.-
dc.contributor.authorCedrés, B.-
dc.contributor.authorGonzález Otero, M.-
dc.contributor.authorJones, D. Heath-
dc.contributor.authorBland Hawthorn, J.-
dc.contributor.otherCentros de Excelencia Severo Ochoa, INSTITUTO DE ASTROFISICA DE ANDALUCIA (IAA), SEV-2017-0709-
dc.contributor.otherUnidad de Excelencia Científica María de Maeztu Centro de Astrobiología del Instituto Nacional de Técnica Aeroespacial y CSIC, MDM-2017-0737-
dc.date.accessioned2021-03-24T11:38:17Z-
dc.date.available2021-03-24T11:38:17Z-
dc.date.issued2020-06-25-
dc.identifier.citationAstronomy and Astrophysics 638: A134 (2020)es
dc.identifier.issn0004-6361-
dc.identifier.otherhttps://www.aanda.org/articles/aa/abs/2020/06/aa37697-20/aa37697-20.html-
dc.identifier.urihttp://hdl.handle.net/20.500.12666/128-
dc.description.abstractContext. 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.es
dc.description.sponsorshipThis work was supported by the project Evolution of Galaxies, of reference AYA2014-58861-C3-1-P and AYA2017-88007-C3-1-P, within the "Programa estatal de fomento de la investigacion cientifica y tecnica de excelencia del Plan Estatal de Investigacion Cientifica y Tecnica y de Innovacion (2013-2016)" of the "Agencia Estatal de Investigacion del Ministerio de Ciencia, Innovacion y Universidades", and co-financed by the FEDER "Fondo Europeo de Desarrollo Regional". JAD is grateful for the support from the UNAM-DGAPA-PASPA 2019 program, the UNAM-CIC, the Canary Islands CIE: Tricontinental Atlantic Campus 2017, and the kind hospitality of the IAC. MP acknowledges financial supports from the Ethiopian Space Science and Technology Institute (ESSTI) under the Ethiopian Ministry of Innovation and Technology (MoIT), and from the Spanish Ministry of Economy and Competitiveness (MINECO) through projects AYA2013-42227-P and AYA2016-76682C3-1-P. APG, MSP and RPM were supported by the PNAYA project: AYA2017-88007-C3-2-P. MC and APG are also funded by Spanish State Research Agency grant MDM-2017-0737 (Unidad de Excelencia Maria de Maeztu CAB). EJA acknowledges support from the Spanish Government Ministerio de Ciencia, Innovacion y Universidades though grant PGC2018-095049-B-C21. M.P. and E.J.A. also acknowledge support from the State Agency for Research of the Spanish MCIU through the Center of Excellence Severo Ochoa award for the Instituto de Astrofisica de Andalucia (SEV-2017-0709). JG receives support through the project AyA2018-RTI-096188-B-100. MALL acknowledges support from the Carlsberg Foundation via a Semper Ardens grant (CF15-0384). JIGS receives support through the Proyecto Puente 52.JU25.64661 (2018) funded by Sodercan S.A. and the Universidad de Cantabria, and PGC2018-099705-B-100 funded by the Ministerio de Ciencia, Innovacion y Universidades. Based on observations made with the Gran Telescopio Canarias (GTC), installed in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofisica de Canarias, in the island of La Palma. This work is (partly) based on data obtained with the instrument OSIRIS, built by a Consortium led by the Instituto de Astrofisica de Canarias in collaboration with the Instituto de Astronomia of the Universidad Autonoma de Mexico. OSIRIS was funded by GRANTECAN and the National Plan of Astronomy and Astrophysics of the Spanish Government; With funding from the Spanish government through the "María de Maeztu Unit of Excellence" accreditation (MDM-2017-0737).es
dc.language.isoenges
dc.publisherEDP Scienceses
dc.relationinfo:eu-repo/grantAgreement/MINECO//AYA2014-58861-C3-1-P/ES/EVOLUCION DE GALAXIAS/-
dc.relationinfo:eu-repo/grantAgreement/ES/MINECO/AYA2013-42227-P-
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/AYA2017-88007-C3-2-P/ES/EVOLUCION DE GALAXIAS EN CUMULOS/-
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PGC2018-095049-B-C21-
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-096188-B-I00-
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PGC2018-099705-B-I00/ES/RADIO CUASARES OSCURECIDOS DESVELADOS/-
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/AYA2017-88007-C3-1-P/ES/EVOLUCION DE GALAXIAS/-
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/AYA2016-76682C3-1-P/ES/AGN, DEL UNIVERSO LOCAL A DISTANCIAS COSMOLOGICAS. DEL MOTOR CENTRAL A LA GALAXIA ANFITRIONA Y SU ENTORNO/-
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationales
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/-
dc.subjectGalaxies: generales
dc.subjectMethods: statisticales
dc.titleGalaxy classification: Deep learning on the OTELO and COSMOS databaseses
dc.typeinfo:eu-repo/semantics/articlees
dc.contributor.orcidDe Diego, J. A. https://orcid.org/0000-0001-7040-069X-
dc.identifier.doi10.1051/0004-6361/202037697-
dc.identifier.e-issn1432-0746-
dc.contributor.funderMinisterio de Economía y Competitividad (MINECO)-
dc.contributor.funderAgencia Estatal de Investigación (AEI)-
dc.description.peerreviewedPeer reviewes
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion-
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess-
dc.type.coarhttp://purl.org/coar/resource_type/c_6501-
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