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dc.contributor.authorBlanco Pillado, José Juan
dc.contributor.authorSousa, Kepa ORCID
dc.contributor.authorÁlvarez Urquiola, Mikel ORCID
dc.date.accessioned2020-11-03T11:06:02Z
dc.date.available2020-11-03T11:06:02Z
dc.date.issued2020-05-27
dc.identifier.citationJournal of High Energy Physics 2019 5 : (2020) // Article ID 142es_ES
dc.identifier.issn1029-8479
dc.identifier.urihttp://hdl.handle.net/10810/47625
dc.description.abstractPhenomenologically interesting scalar potentials are highly atypical in generic random landscapes. We develop the mathematical techniques to generate constrained random potentials, i.e.Slepian models, which can globally represent low-probability realizations of the landscape. We give analytical as well as numerical methods to construct these Slepian models for constrained realizations of a full Gaussian random field around critical as well as inflection points. We use these techniques to numerically generate in an efficient way a large number of minima at arbitrary heights of the potential and calculate their non-perturbative decay rate. Furthermore, we also illustrate how to use these methods by obtaining statistical information about the distribution of observables in an inflationary inflection point constructed within these models.es_ES
dc.description.sponsorshipWe are grateful to Alex Vilenkin, Masaki Yamada and Jeremy M. Wachter for useful discussions, and to Jonathan Frazer for discussions and collaboration at the early stages of this project. This work was supported in part by the Spanish Ministry MINECO grant (FPA2015-64041-C2-1P), the MCIU/AEI/FEDER grant (PGC2018-094626-B-C21), the Basque Government grant (IT-979-16), the University of the Basque Country grant (PIF17/74), the Basque Foundation for Science (IKERBASQUE) and the Czech science foundation GA ~ CR grant (19-01850S). The numerical work necessary to carry out this research has been possible thanks to the computing infrastructure of the ARINA cluster at the University of the Basque Country, UPV/EHU.es_ES
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/FPA2015-64041-C2-1Pes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectcosmology of theories beyond the SMes_ES
dc.subjectstochastic processeses_ES
dc.subjectsuperstring vacuaes_ES
dc.subjectfalse vacuumes_ES
dc.subjectfatees_ES
dc.titleSlepian models for Gaussian random landscapeses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holderThis article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited.es_ES
dc.rights.holderAtribución 3.0 España*
dc.relation.publisherversionhttps://link.springer.com/article/10.1007/JHEP05(2020)142es_ES
dc.identifier.doi10.1007/JHEP05(2020)142
dc.departamentoesFísica teórica e historia de la cienciaes_ES
dc.departamentoeuFisika teorikoa eta zientziaren historiaes_ES


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This article is distributed under the terms of the Creative Commons
Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in
any medium, provided the original author(s) and source are credited.
Except where otherwise noted, this item's license is described as This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited.