The miniJPAS survey quasar selection – II: machine learning classification with photometric measurements and uncertainties (2023)
- Authors:
- USP affiliated authors: ABRAMO, LUIS RAUL WEBER - IF ; RODRIGUES, NATÁLIA VILLA NOVA - IF
- Unidade: IF
- DOI: 10.1093/mnras/stac2836
- Subjects: REDES NEURAIS; GALÁXIAS; COSMOLOGIA
- Language: Inglês
- Imprenta:
- Publisher: Oxford University Press
- Publisher place: Oxford
- Date published: 2023
- Source:
- Título: Monthly Notices of the Royal Astronomical Society
- Volume/Número/Paginação/Ano: v. 520, n. 3, p. 3494–3509, 2023
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
RODRIGUES, Natália Villa Nova e SANTI, Natali Soler Matubaro de e ABRAMO, Luis Raul Weber. The miniJPAS survey quasar selection – II: machine learning classification with photometric measurements and uncertainties. Monthly Notices of the Royal Astronomical Society, v. 520, n. 3, p. 3494–3509, 2023Tradução . . Disponível em: https://doi.org/10.1093/mnras/stac2836. Acesso em: 24 jan. 2026. -
APA
Rodrigues, N. V. N., Santi, N. S. M. de, & Abramo, L. R. W. (2023). The miniJPAS survey quasar selection – II: machine learning classification with photometric measurements and uncertainties. Monthly Notices of the Royal Astronomical Society, 520( 3), 3494–3509. doi:10.1093/mnras/stac2836 -
NLM
Rodrigues NVN, Santi NSM de, Abramo LRW. The miniJPAS survey quasar selection – II: machine learning classification with photometric measurements and uncertainties [Internet]. Monthly Notices of the Royal Astronomical Society. 2023 ; 520( 3): 3494–3509.[citado 2026 jan. 24 ] Available from: https://doi.org/10.1093/mnras/stac2836 -
Vancouver
Rodrigues NVN, Santi NSM de, Abramo LRW. The miniJPAS survey quasar selection – II: machine learning classification with photometric measurements and uncertainties [Internet]. Monthly Notices of the Royal Astronomical Society. 2023 ; 520( 3): 3494–3509.[citado 2026 jan. 24 ] Available from: https://doi.org/10.1093/mnras/stac2836 - The miniJPAS survey quasar selection – I: mock catalogues for classification
- The miniJPAS survey quasar selection: IV. Classification and redshift estimation with SQUEzE
- The miniJPAS survey quasar selection
- High-fidelity reproduction of central galaxy joint distributions with neural networks
- The information of attribute uncertainties: what convolutional neural networks can learn about errors in input data
- Classificação de quasares, estrelas e galáxias com técnicas de aprendizagem automática
- Characterizing large-scale structure tracers with machine learning
- Mimicking the halo–galaxy connection using machine learning
- The multi-tracer optimal estimator applied to VIPERS
- The miniJPAS survey: white dwarf science with 56 optical filters
Informações sobre o DOI: 10.1093/mnras/stac2836 (Fonte: oaDOI API)
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