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

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.

7 pages, 5 figures. To be appeared in Proceedings of the 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research

References in corpus (3)

• An Introduction to PYTHIA 8.2
• DELPHES 3, A modular framework for fast simulation of a generic collider experiment
• SoftKiller, a particle-level pileup removal method

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