Particle Generative Adversarial Networks for full-event simulation at the LHC and their application to pileup description
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Peer-reviewed
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Authors
Arjona Martínez, Jesus
Nguyen, Thong Q
Pierini, Maurizio
Spiropulu, Maria
Vlimant, Jean-Roch
Abstract
jats:titleAbstract</jats:title> jats:pWe 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>
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Journal Title
Journal of Physics: Conference Series
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Journal ISSN
1742-6588
1742-6596
1742-6596
Volume Title
1525
Publisher
IOP Publishing