Galaxy morphological classification catalogue of the Dark Energy Survey Year 3 data with convolutional neural networks (2021)
- Authors:
- Autor USP: SILVA, MICHEL AGUENA DA - IF
- Unidade: IF
- DOI: 10.1093/mnras/stab2142
- Assunto: GALÁXIAS
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: Oxford University Press
- Publisher place: Oxford
- Date published: 2021
- Source:
- Título: Monthly Notices of the Royal Astronomical Society
- Volume/Número/Paginação/Ano: v. 507, n. 3, p. 4425–4444, 2021
- Este periódico é de assinatura
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: hybrid
- Licença: cc-by
-
ABNT
CHENG, Ting-Yun e SILVA, Michel Aguena da. Galaxy morphological classification catalogue of the Dark Energy Survey Year 3 data with convolutional neural networks. Monthly Notices of the Royal Astronomical Society, v. 507, n. 3, p. 4425–4444, 2021Tradução . . Disponível em: https://doi.org/10.1093/mnras/stab2142. Acesso em: 29 dez. 2025. -
APA
Cheng, T. -Y., & Silva, M. A. da. (2021). Galaxy morphological classification catalogue of the Dark Energy Survey Year 3 data with convolutional neural networks. Monthly Notices of the Royal Astronomical Society, 507( 3), 4425–4444. doi:10.1093/mnras/stab2142 -
NLM
Cheng T-Y, Silva MA da. Galaxy morphological classification catalogue of the Dark Energy Survey Year 3 data with convolutional neural networks [Internet]. Monthly Notices of the Royal Astronomical Society. 2021 ; 507( 3): 4425–4444.[citado 2025 dez. 29 ] Available from: https://doi.org/10.1093/mnras/stab2142 -
Vancouver
Cheng T-Y, Silva MA da. Galaxy morphological classification catalogue of the Dark Energy Survey Year 3 data with convolutional neural networks [Internet]. Monthly Notices of the Royal Astronomical Society. 2021 ; 507( 3): 4425–4444.[citado 2025 dez. 29 ] Available from: https://doi.org/10.1093/mnras/stab2142 - Dark Energy Survey Year 3 results: Optimizing the lens sample in a combined galaxy clustering and galaxy-galaxy lensing analysis
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Informações sobre o DOI: 10.1093/mnras/stab2142 (Fonte: oaDOI API)
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