Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Martín de los Rios"'
Publikováno v:
European Physical Journal C: Particles and Fields, Vol 83, Iss 12, Pp 1-14 (2023)
Abstract Machine-learned likelihoods (MLL) combines machine-learning classification techniques with likelihood-based inference tests to estimate the experimental sensitivity of high-dimensional data sets. We extend the MLL method by including kernel
Externí odkaz:
https://doaj.org/article/27abc3497fb14803b94a5c3ee3930c74
Autor:
Martín De los Rios
Publikováno v:
Scopus
Repositório Institucional da UNESP
Universidade Estadual Paulista (UNESP)
instacron:UNESP
Repositório Institucional da UNESP
Universidade Estadual Paulista (UNESP)
instacron:UNESP
In this work we present the results of the study of the cosmic microwave background TT power spectrum through auto-encoders in which the latent variables are the cosmological parameters. This method was trained and calibrated using a data-set compose
Publikováno v:
Proceedings of 41st International Conference on High Energy physics — PoS(ICHEP2022).
Autor:
Valeria Coenda, Martín de los Rios, Hernán Muriel, Sofía A Cora, Héctor J Martínez, Andrés N Ruiz, Cristian A Vega-Martínez
Publikováno v:
Scopus
Repositório Institucional da UNESP
Universidade Estadual Paulista (UNESP)
instacron:UNESP
Repositório Institucional da UNESP
Universidade Estadual Paulista (UNESP)
instacron:UNESP
We connect galaxy properties with their orbital classification by analysing a sample of galaxies with stellar mass $M_{\star} \geq 10^{8.5}h^{-1}M_\odot$ residing in and around massive and isolated galaxy clusters with mass $M_{200} > 10^{15}h^{-1}M_
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::93b4dbec817e7d05710cff858500cd2d
http://arxiv.org/abs/2112.01552
http://arxiv.org/abs/2112.01552
Autor:
Sofía A. Cora, Cristian A. Vega-Martínez, Andrés N. Ruiz, H. Muriel, Héctor J. Martínez, Martín de los Rios, Valeria Coenda
Publikováno v:
Web of Science
Repositório Institucional da UNESP
Universidade Estadual Paulista (UNESP)
instacron:UNESP
CONICET Digital (CONICET)
Consejo Nacional de Investigaciones Científicas y Técnicas
instacron:CONICET
Repositório Institucional da UNESP
Universidade Estadual Paulista (UNESP)
instacron:UNESP
CONICET Digital (CONICET)
Consejo Nacional de Investigaciones Científicas y Técnicas
instacron:CONICET
We present the ROGER (Reconstructing Orbits of Galaxies in Extreme Regions) code, which uses three different machine learning techniques to classify galaxies in, and around, clusters, according to their projected phase-space position. We use a sample
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7e799843f870ef0a6cb3b968abf20d98
Akademický článek
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Autor:
Elizabeth Johana Gonzalez, L Héctor Cuevas, Martín de los Rios, Tania Aguirre Tagliaferro, J R Mariano Domínguez, G. A. Oio, José Luis Nilo Castellón, Carlos Valotto, Daniel Hernández Lang
Publikováno v:
CONICET Digital (CONICET)
Consejo Nacional de Investigaciones Científicas y Técnicas
instacron:CONICET
Consejo Nacional de Investigaciones Científicas y Técnicas
instacron:CONICET
Merging galaxy clusters allow for the study of different mass components, dark and baryonic, separately. Also, their occurrence enables to test the ΛCDM scenario, which can be used to put constraints on the selfinteracting cross-section of the dark-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1cdfb2a1e83f6bf17c98f3d3a231637e
https://www.aanda.org/10.1051/0004-6361/201732003
https://www.aanda.org/10.1051/0004-6361/201732003
Merging galaxy systems provides observational evidence of the existence of dark matter and constraints on its properties. Therefore, statistical uniform samples of merging systems would be a powerful tool for several studies. In this work we presents
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d0276cba2f264b2a8a2e8ea56df51a03
http://arxiv.org/abs/1509.02524
http://arxiv.org/abs/1509.02524
Conference
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