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Accelerating the BSM interpretation of LHC data with machine learning

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dc.contributor.authorGianfranco Bertone-
dc.contributor.authorMarc Peter Deisenroth-
dc.contributor.authorJong Soo Kim-
dc.contributor.authorSebastian Liem-
dc.contributor.authorRoberto Ruiz de Austri-
dc.contributor.authorMax Welling-
dc.date.available2019-08-19T02:07:08Z-
dc.date.created2019-04-23-
dc.date.issued2019-03-
dc.identifier.issn2212-6864-
dc.identifier.urihttps://pr.ibs.re.kr/handle/8788114/6018-
dc.description.abstractThe interpretation of Large Hadron Collider (LHC) data in the framework of Beyond the Standard Model (BSM) theories is hampered by the need to run computationally expensive event generators and detector simulators. Performing statistically convergent scans of high-dimensional BSM theories is consequently challenging, and in practice unfeasible for very high-dimensional BSM theories. We present here a new machine learning method that accelerates the interpretation of LHC data, by learning the relationship between BSM theory parameters and data. As a proof-of-concept, we demonstrate that this technique accurately predicts natural SUSY signal events in two signal regions at the High Luminosity LHC, up to four orders of magnitude faster than standard techniques. The new approach makes it possible to rapidly and accurately reconstruct the theory parameters of complex BSM theories, should an excess in the data be discovered at the LHC. © 2019 Elsevier B.V-
dc.description.uri1-
dc.language영어-
dc.publisherELSEVIER SCIENCE BV-
dc.titleAccelerating the BSM interpretation of LHC data with machine learning-
dc.typeArticle-
dc.type.rimsART-
dc.identifier.wosid000465292500018-
dc.identifier.scopusid2-s2.0-85063403874-
dc.identifier.rimsid67938-
dc.contributor.affiliatedAuthorJong Soo Kim-
dc.identifier.doi10.1016/j.dark.2019.100293-
dc.identifier.bibliographicCitationPHYSICS OF THE DARK UNIVERSE, v.24, pp.100293-
dc.citation.titlePHYSICS OF THE DARK UNIVERSE-
dc.citation.volume24-
dc.citation.startPage100293-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordPlusDARK-MATTER-
dc.subject.keywordPlusSQUARK-
dc.subject.keywordPlusMODELS-
Appears in Collections:
Center for Fundamental Theory(순수물리이론 연구단) > 1. Journal Papers (저널논문)
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