Constraints on prospective deviations from the cold dark matter model using a Gaussian process
Recently, using Bayesian Machine Learning, a deviation from the cold dark matter model on cosmological scales has been put forward. Such a model might replace the proposed non-gravitational interaction between dark energy and dark matter, and help solve the 0 tension problem. The idea behind the lea...
| Autores: | , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/362733 |
| Acceso en línea: | http://hdl.handle.net/10261/362733 |
| Access Level: | acceso abierto |
| Palabra clave: | H0 tension Bayesian machine learning Gaussian processes |
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Constraints on prospective deviations from the cold dark matter model using a Gaussian processKhurshudyan, MartirosElizalde, EmilioH0 tensionBayesian machine learningGaussian processesRecently, using Bayesian Machine Learning, a deviation from the cold dark matter model on cosmological scales has been put forward. Such a model might replace the proposed non-gravitational interaction between dark energy and dark matter, and help solve the 0 tension problem. The idea behind the learning procedure relies on a generated expansion rate, while the real expansion rate is just used to validate the learned results. In the present work, however, the emphasis is put on a Gaussian Process (GP), with the available () data confirming the possible existence of the already learned deviation. Three cosmological scenarios are considered: a simple one, with an equation-of-state parameter for dark matter =0≠0, and two other models, with corresponding parameters =0+1 and =0+1/(1+). The constraints obtained on the free parameters 0 and 1 hint towards a dynamical nature of the deviation. The dark energy dynamics is also reconstructed, revealing interesting aspects connected with the 0 tension problem. It is concluded, however, that improved tools and more data are needed, to reach a better understanding of the reported deviation.M.K. has been supported by a Juan de la Cierva-incorporación grant (IJC2020-042690-I). This research was funded by MICINN (Spain), project PID2019-104397GB-I00 of the Spanish State Research Agency program AEI/10.13039/501100011033, CSIC project 2024AEP171, by the Catalan Government AGAUR project 2021-SGR-00171, and by the program Unidad de Excelencia María de Maeztu CEX2020-001058-M.With funding from the Spanish government through the ‘María de Maeztu Unit of Excelence’ accreditation (CEX2020-001058-M).Peer reviewedMultidisciplinary Digital Publishing InstituteMinisterio de Ciencia, Innovación y Universidades (España)Agencia Estatal de Investigación (España)Consejo Superior de Investigaciones Científicas (España)Generalitat de CatalunyaMinisterio de Ciencia e Innovación (España)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2024202420242024info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/362733reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI//IJC2020-042690-Iinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-104397GB-I00info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/CEX2020-001058-Mhttps://doi.org/10.3390/galaxies12040031Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3627332026-05-22T06:33:51Z |
| dc.title.none.fl_str_mv |
Constraints on prospective deviations from the cold dark matter model using a Gaussian process |
| title |
Constraints on prospective deviations from the cold dark matter model using a Gaussian process |
| spellingShingle |
Constraints on prospective deviations from the cold dark matter model using a Gaussian process Khurshudyan, Martiros H0 tension Bayesian machine learning Gaussian processes |
| title_short |
Constraints on prospective deviations from the cold dark matter model using a Gaussian process |
| title_full |
Constraints on prospective deviations from the cold dark matter model using a Gaussian process |
| title_fullStr |
Constraints on prospective deviations from the cold dark matter model using a Gaussian process |
| title_full_unstemmed |
Constraints on prospective deviations from the cold dark matter model using a Gaussian process |
| title_sort |
Constraints on prospective deviations from the cold dark matter model using a Gaussian process |
| dc.creator.none.fl_str_mv |
Khurshudyan, Martiros Elizalde, Emilio |
| author |
Khurshudyan, Martiros |
| author_facet |
Khurshudyan, Martiros Elizalde, Emilio |
| author_role |
author |
| author2 |
Elizalde, Emilio |
| author2_role |
author |
| dc.contributor.none.fl_str_mv |
Ministerio de Ciencia, Innovación y Universidades (España) Agencia Estatal de Investigación (España) Consejo Superior de Investigaciones Científicas (España) Generalitat de Catalunya Ministerio de Ciencia e Innovación (España) Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72] |
| dc.subject.none.fl_str_mv |
H0 tension Bayesian machine learning Gaussian processes |
| topic |
H0 tension Bayesian machine learning Gaussian processes |
| description |
Recently, using Bayesian Machine Learning, a deviation from the cold dark matter model on cosmological scales has been put forward. Such a model might replace the proposed non-gravitational interaction between dark energy and dark matter, and help solve the 0 tension problem. The idea behind the learning procedure relies on a generated expansion rate, while the real expansion rate is just used to validate the learned results. In the present work, however, the emphasis is put on a Gaussian Process (GP), with the available () data confirming the possible existence of the already learned deviation. Three cosmological scenarios are considered: a simple one, with an equation-of-state parameter for dark matter =0≠0, and two other models, with corresponding parameters =0+1 and =0+1/(1+). The constraints obtained on the free parameters 0 and 1 hint towards a dynamical nature of the deviation. The dark energy dynamics is also reconstructed, revealing interesting aspects connected with the 0 tension problem. It is concluded, however, that improved tools and more data are needed, to reach a better understanding of the reported deviation. |
| publishDate |
2024 |
| dc.date.none.fl_str_mv |
2024 2024 2024 2024 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article http://purl.org/coar/resource_type/c_6501 Publisher's version info:eu-repo/semantics/publishedVersion |
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article |
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publishedVersion |
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http://hdl.handle.net/10261/362733 |
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http://hdl.handle.net/10261/362733 |
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Inglés |
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Inglés |
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#PLACEHOLDER_PARENT_METADATA_VALUE# #PLACEHOLDER_PARENT_METADATA_VALUE# #PLACEHOLDER_PARENT_METADATA_VALUE# info:eu-repo/grantAgreement/AEI//IJC2020-042690-I info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-104397GB-I00 info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/CEX2020-001058-M https://doi.org/10.3390/galaxies12040031 Sí |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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Multidisciplinary Digital Publishing Institute |
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Multidisciplinary Digital Publishing Institute |
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reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC instname:Consejo Superior de Investigaciones Científicas (CSIC) |
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