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arXiv · 1912.02748

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

Jesus Arjona Martinez·Thong Q Nguyen·Maurizio Pierini·Maria Spiropulu·Jean-Roch Vlimant
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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.

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Jesus Arjona Martinez, Thong Q Nguyen, Maurizio Pierini, Maria Spiropulu, Jean-Roch Vlimant. 2019-12-05. Particle Generative Adversarial Networks for full-event simulation at the LHC and their application to pileup description. https://doi.org/10.1088/1742-6596/1525/1/012081

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