Photometric redshifts for the S-PLUS Survey: Is machine learning up to the task?

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dc.contributor.authorLima, Erik Vinicius Rodrigues
dc.contributor.authorSodré Junior, Laerte
dc.contributor.authorBom, Clécio Roque
dc.contributor.authorTeixeira, Gabriel da Silva Moreira
dc.contributor.authorNakazono, Lilianne Mariko Izuti
dc.contributor.authorBuzzo, Maria Luisa
dc.contributor.authorQueiroz, Carolina Queiroz de Abreu
dc.contributor.authorHerpich, Fábio R.
dc.contributor.authorCastellon, José Luis Nilo
dc.contributor.authorDantas, Maria Luiza Linhares
dc.contributor.authorDors Junior, Oli Luiz
dc.contributor.authorSouza, Rodrigo Clemente Thom de
dc.contributor.authorAkras, Stavros
dc.contributor.authorJiménez-Teja, Yolanda
dc.contributor.authorKanaan, Antonio
dc.contributor.authorRibeiro, Tiago
dc.contributor.authorSchoennell, William
dc.date.accessioned2025-04-08T13:46:50Z
dc.date.available2025-04-08T13:46:50Z
dc.date.issued22021
dc.description.abstractThe Southern Photometric Local Universe Survey (S-PLUS) is a novel project that aims to map the Southern Hemisphere using a twelve filter system, comprising five broad-band SDSS-like filters and seven narrow-band filters optimized for important stellar features in the local universe. In this paper we use the photometry and morphological information from the first S-PLUS data release (S-PLUS DR1) cross-matched to unWISE data and spectroscopic redshifts from Sloan Digital Sky Survey DR15. We explore three different machine learning methods (Gaussian Processes with GPz and two Deep Learning models made with TensorFlow) and compare them with the currently used template-fitting method in the S-PLUS DR1 to address whether machine learning methods can take advantage of the twelve filter system for photometric redshift prediction. Using tests for accuracy for both single-point estimates such as the calculation of the scatter, bias, and outlier fraction, and probability distribution functions (PDFs) such as the Probability Integral Transform (PIT), the Continuous Ranked Probability Score (CRPS) and the Odds distribution, we conclude that a deep-learning method using a combination of a Bayesian Neural Network and a Mixture Density Network offers the most accurate photometric redshifts for the current test sample. It achieves single-point photometric redshifts with scatter ( ) of 0.023, normalized bias of -0.001, and outlier fraction of 0.64% for galaxies with r_auto magnitudes between 16 and
dc.description.physical15 p.
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.uriCAPES (88887.470064/2019-00; 304819/2017-4) FAPESP (2019/10923-5; 2019/01312-2; 2014/10566-4; 2015/11442-0; 2019/06766-1) CNPq (169181/2017-0; 304819/2017-4)
dc.format.mimetypePDF
dc.identifier.affiliationUniversidade de São Paulo
dc.identifier.affiliationCentro Brasileiro de Pesquisas Físicas
dc.identifier.affiliationCentro Federal de Educação Tecnológica Celso Suckow da Fonseca
dc.identifier.affiliationUniversidade Federal do Rio Grande do Sul
dc.identifier.affiliationUniversidad de La Serena
dc.identifier.affiliationPolish Academy of Sciences
dc.identifier.affiliationUniversidade do Vale do Paraíba
dc.identifier.affiliationUniversidade Estadual de Maringá
dc.identifier.affiliationUniversidade Federal do Paraná
dc.identifier.affiliationNational Observatory of Athens
dc.identifier.affiliationInstituto de Astrofísica de Andalucía
dc.identifier.affiliationUniversidade Federal de Santa Catarina
dc.identifier.bibliographicCitationLIMA, Erik Vinicius Rodrigues et al. Photometric redshifts for the S-PLUS Survey: Is machine learning up to the task? Astronomy and Computing, v. 38, p. 1-15, 2021. Disponível em: https://linkinghub.elsevier.com/retrieve/pii/S2213133721000640.
dc.identifier.doi10.1016/j.ascom.2021.100510
dc.identifier.urihttps://repositorio.univap.br/handle/123456789/821
dc.language.isoen_US
dc.publisherElsevier
dc.rights.holderAstronomy and Computing
dc.subject.keywordGalaxies: distances and redshifts
dc.subject.keywordPhotometric
dc.subject.keywordSurveys
dc.titlePhotometric redshifts for the S-PLUS Survey: Is machine learning up to the task?
dc.typeArtigos de Periódicos

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