| Article | |
| Report number | arXiv:1912.02748 |
| Title | Particle Generative Adversarial Networks for full-event simulation at the LHC and their application to pileup description |
| Author(s) | Arjona Martínez, Jesús (Trinity Coll., Cambridge ; Caltech ; CERN) ; Nguyen, Thong Q. (Caltech) ; Pierini, Maurizio (CERN) ; Spiropulu, Maria (Caltech) ; Vlimant, Jean-Roch (Caltech) |
| Publication | 2020-07-08 |
| Imprint | 2019-12-05 |
| Number of pages | 7 |
| Note | 7 pages, 5 figures. To be appeared in Proceedings of the 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research |
| In: | J. Phys.: Conf. Ser. 1525 (2020) 012081 |
| In: | 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, Saas Fee, Switzerland, 11 - 15 Mar 2019, pp.012081 |
| DOI | 10.1088/1742-6596/1525/1/012081 |
| Subject category | hep-ph ; Particle Physics - Phenomenology ; hep-ex ; Particle Physics - Experiment |
| 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. |
| Copyright/License | preprint: (License: arXiv nonexclusive-distrib 1.0) publication: (License: CC-BY-3.0) |
Corresponding record in: Inspire
Record created 2019-12-21, last modified 2024-06-22