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dc.contributor.authorCole, A
dc.contributor.authorSchachner, A
dc.contributor.authorShiu, G
dc.date.accessioned2021-12-24T00:30:11Z
dc.date.available2021-12-24T00:30:11Z
dc.date.issued2019
dc.identifier.issn1126-6708
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/331755
dc.description.abstractIn this paper, we employ genetic algorithms to explore the landscape of type IIB flux vacua. We show that genetic algorithms can efficiently scan the landscape for viable solutions satisfying various criteria. More specifically, we consider a symmetric $T^{6}$ as well as the conifold region of a Calabi-Yau hypersurface. We argue that in both cases genetic algorithms are powerful tools for finding flux vacua with interesting phenomenological properties. We also compare genetic algorithms to algorithms based on different breeding mechanisms as well as random walk approaches.
dc.publisherSpringer Science and Business Media LLC
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectSuperstring Vacua
dc.subjectFlux compactifications
dc.titleSearching the landscape of flux vacua with genetic algorithms
dc.typeArticle
dc.publisher.departmentDept of Applied Mathematics & Theoretical Physics Student
dc.date.updated2021-12-13T13:12:46Z
prism.issueIdentifier11
prism.numberARTN 045
prism.publicationDate2019
prism.publicationNameJournal of High Energy Physics
prism.startingPage45
prism.volume2019
dc.identifier.doi10.17863/CAM.79204
rioxxterms.versionofrecord10.1007/JHEP11(2019)045
rioxxterms.versionVoR
dc.contributor.orcidCole, A [0000-0001-8035-4308]
dc.identifier.eissn1029-8479
rioxxterms.typeJournal Article/Review
cam.issuedOnline2019-11-08
cam.depositDate2021-12-13
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International