The CAMELS project: expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suites (2023)
- Authors:
- Autor USP: SANTI, NATALÍ SOLER MATUBARO DE - IF
- Unidade: IF
- DOI: 10.3847/1538-4357/ad022a
- Subjects: BURACOS NEGROS; COSMOLOGIA
- Language: Inglês
- Imprenta:
- Publisher: IOP Publishing
- Publisher place: Bristol
- Date published: 2023
- Source:
- Título: Astrophysical Journal
- Volume/Número/Paginação/Ano: v. 956, n. 2, p. 136, 2023
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
NI, Yueying e SANTI, Natali Soler Matubaro de. The CAMELS project: expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suites. Astrophysical Journal, v. 956, n. 2, p. 136, 2023Tradução . . Disponível em: https://doi.org/10.3847/1538-4357/ad022a. Acesso em: 24 jan. 2026. -
APA
Ni, Y., & Santi, N. S. M. de. (2023). The CAMELS project: expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suites. Astrophysical Journal, 956( 2), 136. doi:10.3847/1538-4357/ad022a -
NLM
Ni Y, Santi NSM de. The CAMELS project: expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suites [Internet]. Astrophysical Journal. 2023 ; 956( 2): 136.[citado 2026 jan. 24 ] Available from: https://doi.org/10.3847/1538-4357/ad022a -
Vancouver
Ni Y, Santi NSM de. The CAMELS project: expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suites [Internet]. Astrophysical Journal. 2023 ; 956( 2): 136.[citado 2026 jan. 24 ] Available from: https://doi.org/10.3847/1538-4357/ad022a - A universal equation to predict ''ômega' IND. m' from halo and galaxy catalogs
- Machine learning methods for extracting cosmological information
- Robust field-level likelihood-free inference with galaxies
- Improving cosmological covariance matrices with machine learning
- High-fidelity reproduction of central galaxy joint distributions with neural networks
- Mimicking the halo–galaxy connection using machine learning
Informações sobre o DOI: 10.3847/1538-4357/ad022a (Fonte: oaDOI API)
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