Inferring S8(z) and γ (z) with cosmic growth rate measurements using machine learning
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/245372 |
Resumo: | Measurements of the cosmological parameter S8 provided by cosmic microwave background and large scale structure data reveal some tension between them, suggesting that the clustering features of matter in these early and late cosmological tracers could be different. In this work, we use a supervised learning method designed to solve Bayesian approach to regression, known as Gaussian Processes regression, to quantify the cosmic evolution of S8 up to z ~1.5. For this, we propose a novel approach to find firstly the evolution of the function σ8(z), then we find the function S8(z). As a sub-product we obtain a minimal cosmological modeldependent σ8(z = 0) and S8(z = 0) estimates. We select independent data measurements of the growth rate f (z) and of [ f σ8](z) according to criteria of non-correlated data, then we perform the Gaussian reconstruction of these data sets to obtain the cosmic evolution of σ8(z), S8(z), and the growth index γ (z). Our statistical analyses show that S8(z) is compatible with Planck CDM cosmology; when evaluated at the present time we find σ8(z = 0) = 0.766 ± 0.116 and S8(z = 0) = 0.732 ± 0.115. Applying our methodology to the growth index, we find γ (z = 0) = 0.465±0.140. Moreover, we compare our results with others recently obtained in the literature. In none of these functions, i.e. σ8(z), S8(z), and γ (z), do we find significant deviations from the standard cosmology predictions. |
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Avila, FelipeBernui, ArmandoBonilla, AlexanderNunes, Rafael da Costa2022-07-23T05:02:10Z20221434-6044http://hdl.handle.net/10183/245372001145984Measurements of the cosmological parameter S8 provided by cosmic microwave background and large scale structure data reveal some tension between them, suggesting that the clustering features of matter in these early and late cosmological tracers could be different. In this work, we use a supervised learning method designed to solve Bayesian approach to regression, known as Gaussian Processes regression, to quantify the cosmic evolution of S8 up to z ~1.5. For this, we propose a novel approach to find firstly the evolution of the function σ8(z), then we find the function S8(z). As a sub-product we obtain a minimal cosmological modeldependent σ8(z = 0) and S8(z = 0) estimates. We select independent data measurements of the growth rate f (z) and of [ f σ8](z) according to criteria of non-correlated data, then we perform the Gaussian reconstruction of these data sets to obtain the cosmic evolution of σ8(z), S8(z), and the growth index γ (z). Our statistical analyses show that S8(z) is compatible with Planck CDM cosmology; when evaluated at the present time we find σ8(z = 0) = 0.766 ± 0.116 and S8(z = 0) = 0.732 ± 0.115. Applying our methodology to the growth index, we find γ (z = 0) = 0.465±0.140. Moreover, we compare our results with others recently obtained in the literature. In none of these functions, i.e. σ8(z), S8(z), and γ (z), do we find significant deviations from the standard cosmology predictions.application/pdfengThe European physical journal. C, Particles and fields. Berlin. Vol. 82, no. 7 (July 2022), 594, 10p.Evolucao cosmicaCosmologiaProcessos gaussianosInferring S8(z) and γ (z) with cosmic growth rate measurements using machine learningEstrangeiroinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT001145984.pdf.txt001145984.pdf.txtExtracted Texttext/plain46955http://www.lume.ufrgs.br/bitstream/10183/245372/2/001145984.pdf.txtd6e632c0543a121053b1944df1d29817MD52ORIGINAL001145984.pdfTexto completo (inglês)application/pdf551937http://www.lume.ufrgs.br/bitstream/10183/245372/1/001145984.pdf34312c339d5ac9bd817983b2d8bc52eeMD5110183/2453722023-05-14 03:23:34.734815oai:www.lume.ufrgs.br:10183/245372Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2023-05-14T06:23:34Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
Inferring S8(z) and γ (z) with cosmic growth rate measurements using machine learning |
title |
Inferring S8(z) and γ (z) with cosmic growth rate measurements using machine learning |
spellingShingle |
Inferring S8(z) and γ (z) with cosmic growth rate measurements using machine learning Avila, Felipe Evolucao cosmica Cosmologia Processos gaussianos |
title_short |
Inferring S8(z) and γ (z) with cosmic growth rate measurements using machine learning |
title_full |
Inferring S8(z) and γ (z) with cosmic growth rate measurements using machine learning |
title_fullStr |
Inferring S8(z) and γ (z) with cosmic growth rate measurements using machine learning |
title_full_unstemmed |
Inferring S8(z) and γ (z) with cosmic growth rate measurements using machine learning |
title_sort |
Inferring S8(z) and γ (z) with cosmic growth rate measurements using machine learning |
author |
Avila, Felipe |
author_facet |
Avila, Felipe Bernui, Armando Bonilla, Alexander Nunes, Rafael da Costa |
author_role |
author |
author2 |
Bernui, Armando Bonilla, Alexander Nunes, Rafael da Costa |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Avila, Felipe Bernui, Armando Bonilla, Alexander Nunes, Rafael da Costa |
dc.subject.por.fl_str_mv |
Evolucao cosmica Cosmologia Processos gaussianos |
topic |
Evolucao cosmica Cosmologia Processos gaussianos |
description |
Measurements of the cosmological parameter S8 provided by cosmic microwave background and large scale structure data reveal some tension between them, suggesting that the clustering features of matter in these early and late cosmological tracers could be different. In this work, we use a supervised learning method designed to solve Bayesian approach to regression, known as Gaussian Processes regression, to quantify the cosmic evolution of S8 up to z ~1.5. For this, we propose a novel approach to find firstly the evolution of the function σ8(z), then we find the function S8(z). As a sub-product we obtain a minimal cosmological modeldependent σ8(z = 0) and S8(z = 0) estimates. We select independent data measurements of the growth rate f (z) and of [ f σ8](z) according to criteria of non-correlated data, then we perform the Gaussian reconstruction of these data sets to obtain the cosmic evolution of σ8(z), S8(z), and the growth index γ (z). Our statistical analyses show that S8(z) is compatible with Planck CDM cosmology; when evaluated at the present time we find σ8(z = 0) = 0.766 ± 0.116 and S8(z = 0) = 0.732 ± 0.115. Applying our methodology to the growth index, we find γ (z = 0) = 0.465±0.140. Moreover, we compare our results with others recently obtained in the literature. In none of these functions, i.e. σ8(z), S8(z), and γ (z), do we find significant deviations from the standard cosmology predictions. |
publishDate |
2022 |
dc.date.accessioned.fl_str_mv |
2022-07-23T05:02:10Z |
dc.date.issued.fl_str_mv |
2022 |
dc.type.driver.fl_str_mv |
Estrangeiro info:eu-repo/semantics/article |
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http://hdl.handle.net/10183/245372 |
dc.language.iso.fl_str_mv |
eng |
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eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
The European physical journal. C, Particles and fields. Berlin. Vol. 82, no. 7 (July 2022), 594, 10p. |
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info:eu-repo/semantics/openAccess |
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openAccess |
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