A machine learning approach to galaxy properties: joint redshift-stellar mass probability distributions with Random Forest (2021)
- Authors:
- USP affiliated authors: LIMA, MARCOS VINICIUS BORGES TEIXEIRA - IF ; SILVA, MICHEL AGUENA DA - IF
- Unidade: IF
- DOI: 10.1093/mnras/stab164
- 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. 502, n. 2, p. 2770–2786, 2021
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
MUCESH, S e SILVA, Michel Aguena da e LIMA, Marcos Vinicius Borges Teixeira. A machine learning approach to galaxy properties: joint redshift-stellar mass probability distributions with Random Forest. Monthly Notices of the Royal Astronomical Society, v. 502, n. 2, p. 2770–2786, 2021Tradução . . Disponível em: https://doi.org/10.1093/mnras/stab164. Acesso em: 22 fev. 2026. -
APA
Mucesh, S., Silva, M. A. da, & Lima, M. V. B. T. (2021). A machine learning approach to galaxy properties: joint redshift-stellar mass probability distributions with Random Forest. Monthly Notices of the Royal Astronomical Society, 502( 2), 2770–2786. doi:10.1093/mnras/stab164 -
NLM
Mucesh S, Silva MA da, Lima MVBT. A machine learning approach to galaxy properties: joint redshift-stellar mass probability distributions with Random Forest [Internet]. Monthly Notices of the Royal Astronomical Society. 2021 ; 502( 2): 2770–2786.[citado 2026 fev. 22 ] Available from: https://doi.org/10.1093/mnras/stab164 -
Vancouver
Mucesh S, Silva MA da, Lima MVBT. A machine learning approach to galaxy properties: joint redshift-stellar mass probability distributions with Random Forest [Internet]. Monthly Notices of the Royal Astronomical Society. 2021 ; 502( 2): 2770–2786.[citado 2026 fev. 22 ] Available from: https://doi.org/10.1093/mnras/stab164 - Dark Energy Survey year 3 results: covariance modelling and its impact on parameter estimation and quality of fit
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Informações sobre o DOI: 10.1093/mnras/stab164 (Fonte: oaDOI API)
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