Galaxy morphological classification catalogue of the Dark Energy Survey Year 3 data with convolutional neural networks

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorDurham University-
Autor(es): dc.contributorUniversity Park-
Autor(es): dc.contributorUniversity of Manchester-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorLaboratório Interinstitucional de e-Astronomia – LIneA-
Autor(es): dc.contributorFermi National Accelerator Laboratory-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversity of Cambridge-
Autor(es): dc.contributorUniversity College London-
Autor(es): dc.contributorStanford University-
Autor(es): dc.contributorSLAC National Accelerator Laboratory-
Autor(es): dc.contributorNational Center for Supercomputing Applications-
Autor(es): dc.contributorUniversity of Illinois at Urbana–Champaign-
Autor(es): dc.contributorBarcelona Institute of Science and Technology-
Autor(es): dc.contributorOhio State University-
Autor(es): dc.contributorUniversity of Trieste-
Autor(es): dc.contributorINAF – Osservatorio Astronomico di Trieste-
Autor(es): dc.contributorInstitute for Fundamental Physics of the Universe-
Autor(es): dc.contributorObservatório Nacional-
Autor(es): dc.contributorUniversity of Michigan-
Autor(es): dc.contributorMedioambientales y Tecnológicas (CIEMAT)-
Autor(es): dc.contributorUniversity of Chicago-
Autor(es): dc.contributorUniversity of Pennsylvania-
Autor(es): dc.contributorSanta Cruz Institute for Particle Physics-
Autor(es): dc.contributorUniversity of Oslo-
Autor(es): dc.contributorInstitut d’Estudis Espacials de Catalunya (IEEC)-
Autor(es): dc.contributorCSIC)-
Autor(es): dc.contributorUniversidad Autonoma de Madrid-
Autor(es): dc.contributorUniversity of Queensland-
Autor(es): dc.contributorHarvard & Smithsonian-
Autor(es): dc.contributorUniversity of Arizona-
Autor(es): dc.contributorMacquarie University-
Autor(es): dc.contributorLowell Observatory-
Autor(es): dc.contributorInstitució Catalana de Recerca i Estudis Avançats-
Autor(es): dc.contributorUniversity of Wisconsin–Madison-
Autor(es): dc.contributorPeyton Hall-
Autor(es): dc.contributorUniversity of Southampton-
Autor(es): dc.contributorOak Ridge National Laboratory-
Autor(es): dc.contributorUniversity of Portsmouth-
Autor(es): dc.creatorCheng, Ting-Yun-
Autor(es): dc.creatorConselice, Christopher J.-
Autor(es): dc.creatorAragón-Salamanca, Alfonso-
Autor(es): dc.creatorAguena, M.-
Autor(es): dc.creatorAllam, S.-
Autor(es): dc.creatorAndrade-Oliveira, F.-
Autor(es): dc.creatorAnnis, J.-
Autor(es): dc.creatorBluck, A. F.L.-
Autor(es): dc.creatorBrooks, D.-
Autor(es): dc.creatorBurke, D. L.-
Autor(es): dc.creatorKind, M. Carrasco-
Autor(es): dc.creatorCarretero, J.-
Autor(es): dc.creatorChoi, A.-
Autor(es): dc.creatorCostanzi, M.-
Autor(es): dc.creatorda Costa, L. N.-
Autor(es): dc.creatorPereira, M. E.S.-
Autor(es): dc.creatorde Vicente, J.-
Autor(es): dc.creatorDiehl, H. T.-
Autor(es): dc.creatorDrlica-Wagner, A.-
Autor(es): dc.creatorEckert, K.-
Autor(es): dc.creatorEverett, S.-
Autor(es): dc.creatorEvrard, A. E.-
Autor(es): dc.creatorFerrero, I.-
Autor(es): dc.creatorFosalba, P.-
Autor(es): dc.creatorFrieman, J.-
Autor(es): dc.creatorGarcía-Bellido, J.-
Autor(es): dc.creatorGerdes, D. W.-
Autor(es): dc.creatorGiannantonio, T.-
Autor(es): dc.creatorGruen, D.-
Autor(es): dc.creatorGruendl, R. A.-
Autor(es): dc.creatorGschwend, J.-
Autor(es): dc.creatorGutierrez, G.-
Autor(es): dc.creatorHinton, S. R.-
Autor(es): dc.creatorHollowood, D. L.-
Autor(es): dc.creatorHonscheid, K.-
Autor(es): dc.creatorJames, D. J.-
Autor(es): dc.creatorKrause, E.-
Autor(es): dc.creatorKuehn, K.-
Autor(es): dc.creatorKuropatkin, N.-
Autor(es): dc.creatorLahav, O.-
Autor(es): dc.creatorMaia, M. A.G.-
Autor(es): dc.creatorMarch, M.-
Autor(es): dc.creatorMenanteau, F.-
Autor(es): dc.creatorMiquel, R.-
Autor(es): dc.creatorMorgan, R.-
Autor(es): dc.creatorPaz-Chinchón, F.-
Autor(es): dc.creatorPieres, A.-
Autor(es): dc.creatorMalagón, A.A. Plazas-
Autor(es): dc.creatorRoodman, A.-
Autor(es): dc.creatorSanchez, E.-
Autor(es): dc.creatorScarpine, V.-
Autor(es): dc.creatorSerrano, S.-
Autor(es): dc.creatorSevilla-Noarbe, I.-
Autor(es): dc.creatorSmith, M.-
Autor(es): dc.creatorSoares-Santos, M.-
Autor(es): dc.creatorSuchyta, E.-
Autor(es): dc.creatorSwanson, M. E.C.-
Autor(es): dc.creatorTarle, G.-
Autor(es): dc.creatorThomas, D.-
Data de aceite: dc.date.accessioned2025-08-21T21:40:27Z-
Data de disponibilização: dc.date.available2025-08-21T21:40:27Z-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2021-10-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1093/mnras/stab2142-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/229480-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/229480-
Descrição: dc.descriptionWe present in this paper one of the largest galaxy morphological classification catalogues to date, including over 20 million galaxies, using the Dark Energy Survey (DES) Year 3 data based on convolutional neural networks (CNNs). Monochromatic i-band DES images with linear, logarithmic, and gradient scales, matched with debiased visual classifications from the Galaxy Zoo 1 (GZ1) catalogue, are used to train our CNN models. With a training set including bright galaxies (16 ≤ i < 18) at low redshift (z < 0.25), we furthermore investigate the limit of the accuracy of our predictions applied to galaxies at fainter magnitude and at higher redshifts. Our final catalogue covers magnitudes 16 ≤ i < 21, and redshifts z < 1.0, and provides predicted probabilities to two galaxy types – ellipticals and spirals (disc galaxies). Our CNN classifications reveal an accuracy of over 99 per cent for bright galaxies when comparing with the GZ1 classifications (i < 18). For fainter galaxies, the visual classification carried out by three of the co-authors shows that the CNN classifier correctly categorizes discy galaxies with rounder and blurred features, which humans often incorrectly visually classify as ellipticals. As a part of the validation, we carry out one of the largest examinations of non-parametric methods, including ∼100,000 galaxies with the same coverage of magnitude and redshift as the training set from our catalogue. We find that the Gini coefficient is the best single parameter discriminator between ellipticals and spirals for this data set.-
Descrição: dc.descriptionCentre of Extragalactic Astronomy Durham University, Stockton Road-
Descrição: dc.descriptionSchool of Physics and Astronomy University of Nottingham University Park-
Descrição: dc.descriptionJodrell Bank Centre for Astrophysics University of Manchester, Oxford Road-
Descrição: dc.descriptionDepartamento de Física Matemática Instituto de Física Universidade de São Paulo, CP 66318, SP-
Descrição: dc.descriptionLaboratório Interinstitucional de e-Astronomia – LIneA, Rua Gal. José Cristino 77, RJ-
Descrição: dc.descriptionFermi National Accelerator Laboratory, PO Box 500-
Descrição: dc.descriptionInstituto de Física Teórica Universidade Estadual Paulista-
Descrição: dc.descriptionCavendish Laboratory Astrophysics Group University of Cambridge, Madingley Road-
Descrição: dc.descriptionKavli Institute for Cosmology University of Cambridge, Madingley Road-
Descrição: dc.descriptionDepartment of Physics & Astronomy University College London, Gower Street-
Descrição: dc.descriptionKavli Institute for Particle Astrophysics & Cosmology Stanford University, PO Box 2450-
Descrição: dc.descriptionSLAC National Accelerator Laboratory-
Descrição: dc.descriptionCenter for Astrophysical Surveys National Center for Supercomputing Applications, 1205 West Clark Street-
Descrição: dc.descriptionDepartment of Astronomy University of Illinois at Urbana–Champaign, 1002 W. Green Street-
Descrição: dc.descriptionInstitut de Física d’Altes Energies (IFAE) Barcelona Institute of Science and Technology Campus UAB-
Descrição: dc.descriptionCenter for Cosmology and Astro-Particle Physics Ohio State University-
Descrição: dc.descriptionAstronomy Unit Department of Physics University of Trieste, Via Tiepolo 11-
Descrição: dc.descriptionINAF – Osservatorio Astronomico di Trieste, Via G. B. Tiepolo 11-
Descrição: dc.descriptionInstitute for Fundamental Physics of the Universe, Via Beirut 2-
Descrição: dc.descriptionObservatório Nacional, Rua Gal. José Cristino 77-
Descrição: dc.descriptionDepartment of Physics University of Michigan-
Descrição: dc.descriptionCentro de Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT)-
Descrição: dc.descriptionDepartment of Astronomy and Astrophysics University of Chicago-
Descrição: dc.descriptionKavli Institute for Cosmological Physics University of Chicago-
Descrição: dc.descriptionDepartment of Physics and Astronomy University of Pennsylvania-
Descrição: dc.descriptionSanta Cruz Institute for Particle Physics-
Descrição: dc.descriptionDepartment of Astronomy University of Michigan-
Descrição: dc.descriptionInstitute of Theoretical Astrophysics University of Oslo, PO Box 1029 Blindern-
Descrição: dc.descriptionInstitut d’Estudis Espacials de Catalunya (IEEC)-
Descrição: dc.descriptionInstitute of Space Sciences (ICE CSIC) Campus UAB, Carrer de Can Magrans, s/n-
Descrição: dc.descriptionInstituto de Fisica Teorica UAM/CSIC Universidad Autonoma de Madrid-
Descrição: dc.descriptionInstitute of Astronomy University of Cambridge, Madingley Road-
Descrição: dc.descriptionDepartment of Physics Stanford University, 382 Via Pueblo Mall-
Descrição: dc.descriptionSchool of Mathematics and Physics University of Queensland-
Descrição: dc.descriptionDepartment of Physics Ohio State University-
Descrição: dc.descriptionCenter for Astrophysics Harvard & Smithsonian, 60 Garden Street-
Descrição: dc.descriptionDepartment of Astronomy/Steward Observatory University of Arizona, 933 North Cherry Avenue-
Descrição: dc.descriptionAustralian Astronomical Optics Macquarie University-
Descrição: dc.descriptionLowell Observatory, 1400 Mars Hill Road-
Descrição: dc.descriptionInstitució Catalana de Recerca i Estudis Avançats-
Descrição: dc.descriptionPhysics Department University of Wisconsin–Madison, 2320 Chamberlin Hall, 1150 University Avenue-
Descrição: dc.descriptionDepartment of Astrophysical Sciences Princeton University Peyton Hall-
Descrição: dc.descriptionSchool of Physics and Astronomy University of Southampton-
Descrição: dc.descriptionComputer Science and Mathematics Division Oak Ridge National Laboratory-
Descrição: dc.descriptionInstitute of Cosmology and Gravitation University of Portsmouth-
Descrição: dc.descriptionInstituto de Física Teórica Universidade Estadual Paulista-
Formato: dc.format4425-4444-
Idioma: dc.languageen-
Relação: dc.relationMonthly Notices of the Royal Astronomical Society-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectCatalogues-
Palavras-chave: dc.subjectGalaxies: structure-
Palavras-chave: dc.subjectMethods: data analysis-
Palavras-chave: dc.subjectMethods: observational-
Título: dc.titleGalaxy morphological classification catalogue of the Dark Energy Survey Year 3 data with convolutional neural networks-
Tipo de arquivo: dc.typelivro digital-
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