Lima, Erik Vinicius RodriguesSodré Junior, LaerteBom, Clécio RoqueTeixeira, Gabriel da Silva MoreiraNakazono, Lilianne Mariko IzutiBuzzo, Maria LuisaQueiroz, Carolina Queiroz de AbreuHerpich, Fábio R.Castellon, José Luis NiloDantas, Maria Luiza LinharesDors Junior, Oli LuizSouza, Rodrigo Clemente Thom deAkras, StavrosJiménez-Teja, YolandaKanaan, AntonioRibeiro, TiagoSchoennell, William2025-04-082025-04-08https://repositorio.univap.br/handle/123456789/821The 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 andPDFen-USArtigos de PeriódicosAstronomy and Computing10.1016/j.ascom.2021.100510Galaxies: distances and redshiftsPhotometricSurveysLIMA, 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.Universidade de São PauloCentro Brasileiro de Pesquisas FísicasCentro Federal de Educação Tecnológica Celso Suckow da FonsecaUniversidade Federal do Rio Grande do SulUniversidad de La SerenaPolish Academy of SciencesUniversidade do Vale do ParaíbaUniversidade Estadual de MaringáUniversidade Federal do ParanáNational Observatory of AthensInstituto de Astrofísica de AndalucíaUniversidade Federal de Santa Catarina