Dark energy survey year 3 results: Photometric data set for cosmology (2021)
- Authors:
- USP affiliated authors: LIMA, MARCOS VINICIUS BORGES TEIXEIRA - IF ; SILVA, MICHEL AGUENA DA - IF
- Unidade: IF
- DOI: 10.3847/1538-4365/abeb66
- Assunto: COSMOLOGIA
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: American Astronomical Society
- Publisher place: Washington
- Date published: 2021
- Source:
- Título: Astrophysical Journal Supplement Series
- Volume/Número/Paginação/Ano: v. 254, Number 2, 2021
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
SEVILLA-NOARBE, I e SILVA, Michel Aguena da e LIMA, Marcos Vinicius Borges Teixeira. Dark energy survey year 3 results: Photometric data set for cosmology. Astrophysical Journal Supplement Series, v. 254, 2021Tradução . . Disponível em: https://doi.org/10.3847/1538-4365/abeb66. Acesso em: 03 mar. 2026. -
APA
Sevilla-Noarbe, I., Silva, M. A. da, & Lima, M. V. B. T. (2021). Dark energy survey year 3 results: Photometric data set for cosmology. Astrophysical Journal Supplement Series, 254. doi:10.3847/1538-4365/abeb66 -
NLM
Sevilla-Noarbe I, Silva MA da, Lima MVBT. Dark energy survey year 3 results: Photometric data set for cosmology [Internet]. Astrophysical Journal Supplement Series. 2021 ; 254[citado 2026 mar. 03 ] Available from: https://doi.org/10.3847/1538-4365/abeb66 -
Vancouver
Sevilla-Noarbe I, Silva MA da, Lima MVBT. Dark energy survey year 3 results: Photometric data set for cosmology [Internet]. Astrophysical Journal Supplement Series. 2021 ; 254[citado 2026 mar. 03 ] Available from: https://doi.org/10.3847/1538-4365/abeb66 - Dark Energy Survey year 3 results: covariance modelling and its impact on parameter estimation and quality of fit
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- A machine learning approach to galaxy properties: joint redshift-stellar mass probability distributions with Random Forest
Informações sobre o DOI: 10.3847/1538-4365/abeb66 (Fonte: oaDOI API)
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