Particle Generative Adversarial Networks for full-event simulation at the LHC and their application to pileup description
Authors
Arjona Martínez, Jesus
Nguyen, Thong Q
Pierini, Maurizio
Spiropulu, Maria
Vlimant, Jean-Roch
Publication Date
2020-04Journal Title
Journal of Physics: Conference Series
ISSN
1742-6588
Publisher
IOP Publishing
Volume
1525
Issue
1
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Arjona Martínez, J., Nguyen, T. Q., Pierini, M., Spiropulu, M., & Vlimant, J. (2020). Particle Generative Adversarial Networks for full-event simulation at the LHC and their application to pileup description. Journal of Physics: Conference Series, 1525 (1)https://doi.org/10.1088/1742-6596/1525/1/012081
Abstract
Abstract: 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.
Keywords
Paper
Identifiers
jpcs_1525_1_012081, j15251081
External DOI: https://doi.org/10.1088/1742-6596/1525/1/012081
This record's URL: https://www.repository.cam.ac.uk/handle/1810/307696
Rights
Licence:
http://creativecommons.org/licenses/by/3.0/