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dc.contributor.authorArjona Martínez, Jesus
dc.contributor.authorNguyen, Thong Q
dc.contributor.authorPierini, Maurizio
dc.contributor.authorSpiropulu, Maria
dc.contributor.authorVlimant, Jean-Roch
dc.date.accessioned2022-02-11T09:30:48Z
dc.date.available2022-02-11T09:30:48Z
dc.date.issued2020-04-01
dc.identifier.issn1742-6588
dc.identifier.otherjpcs_1525_1_012081
dc.identifier.otherj15251081
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/333904
dc.description.abstract<jats:title>Abstract</jats:title> <jats:p>We investigate how a Generative Adversarial Network could be used to generate a list of particle four-momenta from LHC proton collisions, allowing one to define a generative model that could abstract from the irregularities of typical detector geometries. As an example of application, we show how such an architecture could be used as a generator of LHC parasitic collisions (pileup). We present two approaches to generate the events: unconditional generator and generator conditioned on missing transverse energy. We assess generation performances in a realistic LHC data-analysis environment, with a pileup mitigation algorithm applied.</jats:p>
dc.languageen
dc.publisherIOP Publishing
dc.subjectPaper
dc.titleParticle Generative Adversarial Networks for full-event simulation at the LHC and their application to pileup description
dc.typeArticle
dc.date.updated2022-02-11T09:30:48Z
prism.issueIdentifier1
prism.publicationNameJournal of Physics: Conference Series
prism.volume1525
dc.identifier.doi10.17863/CAM.81320
rioxxterms.versionofrecord10.1088/1742-6596/1525/1/012081
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/3.0/
dc.identifier.eissn1742-6596


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