Photometric redshifts for the S-PLUS Survey: Is machine learning up to the task? (2022)
Lima, Erik Rodrigues de 
; Sodre Junior, Laerte ; Nakazono, L.; Buzzo, Maria Luisa; Silva, Carolina Queiroz de Abreu ; Herpich, Fábio R.
Source: Astronomy and Computing. Unidades: IAG, IF
Subjects: ASTROFÍSICA, COSMOLOGIA, FOTÔMETROS, GALÁXIAS, SOFTWARES, ANÁLISE DE DADOS, PROBABILIDADE
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LIMA, Erik Rodrigues de et al. Photometric redshifts for the S-PLUS Survey: Is machine learning up to the task?. Astronomy and Computing, v. 38, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.ascom.2021.100510. Acesso em: 23 nov. 2025.APA
Lima, E. R. de, Sodre Junior, L., Nakazono, L., Buzzo, M. L., Silva, C. Q. de A., & Herpich, F. R. (2022). Photometric redshifts for the S-PLUS Survey: Is machine learning up to the task? Astronomy and Computing, 38. doi:10.1016/j.ascom.2021.100510NLM
Lima ER de, Sodre Junior L, Nakazono L, Buzzo ML, Silva CQ de A, Herpich FR. Photometric redshifts for the S-PLUS Survey: Is machine learning up to the task? [Internet]. Astronomy and Computing. 2022 ; 38[citado 2025 nov. 23 ] Available from: https://doi.org/10.1016/j.ascom.2021.100510Vancouver
Lima ER de, Sodre Junior L, Nakazono L, Buzzo ML, Silva CQ de A, Herpich FR. Photometric redshifts for the S-PLUS Survey: Is machine learning up to the task? [Internet]. Astronomy and Computing. 2022 ; 38[citado 2025 nov. 23 ] Available from: https://doi.org/10.1016/j.ascom.2021.100510
