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Particle Generative Adversarial Networks for full-event simulation at the LHC and their application to pileup description

Published version
Peer-reviewed

Change log

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>

Description

Keywords

5106 Nuclear and Plasma Physics, 51 Physical Sciences

Journal Title

Journal of Physics: Conference Series

Conference Name

Journal ISSN

1742-6588
1742-6596

Volume Title

1525

Publisher

IOP Publishing