jpcsJournal of Physics: Conference SeriesJ. Phys.: Conf. Ser.1742-65881742-6596IOP PublishingJPCS_1525_1_01208110.1088/1742-6596/1525/1/012081J15251081PaperParticle Generative Adversarial Networks for full-event simulation at the LHC and their application to pileup descriptionArjona MartínezJesus123ja618@cam.ac.ukNguyenThong Q2thong@caltech.eduPieriniMaurizio3maurizio.pierini@cern.chSpiropuluMaria2smaria@caltech.eduVlimantJean-Roch2jvlimant@caltech.edu University of Cambridge, Trinity Ln, Cambridge CB2 1TN, UK California Institute of Technology, 1200 E. California Blvd, Pasadena, CA 91125 CERN, CH-1211 Geneva, Switzerland 010420200104202015251012081Published under licence by IOP Publishing Ltd2020 Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.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.