Jul 6 – 8, 2021
Europe/Zurich timezone

Particle Cloud Generation with Message Passing GANs

Jul 7, 2021, 4:20 PM


Raghav Kansal (Univ. of California San Diego (US))


There has been significant development recently in generative models for accelerating LHC simulations. Work on simulating jets has primarily used image-based representations, which tend to be sparse and of limited resolution. We advocate for the more natural ‘particle cloud’ representation of jets, i.e. as a set of particles in momentum space, and discuss four physics- and computer-vision-inspired metrics: (1) the 1-Wasserstein distance between high- and low-level feature distributions; (2) a new Fréchet ParticleNet Distance; (3) the coverage; and (4) the minimum matching distance as means of quantitatively and holistically evaluating generated particle clouds. We then present our new message-passing generative adversarial network (MPGAN), which has excellent performance on gluon, top quark, and lighter quark jets on all metrics, evaluated against real samples via bootstrapping as well as existing point cloud GANs, and shows promise for use in HEP.

Academic Rank PhD student
Affiliation UC San Diego

Primary authors

Raghav Kansal (Univ. of California San Diego (US)) Javier Mauricio Duarte (Univ. of California San Diego (US)) Dr Hao Su Maurizio Pierini (CERN) Mary Touranakou (National and Kapodistrian University of Athens (GR)) Breno Orzari (UNESP - Universidade Estadual Paulista (BR)) Thiago Tomei Fernandez (UNESP - Universidade Estadual Paulista (BR)) Jean-Roch Vlimant (California Institute of Technology (US)) Dr Dimitrios Gunopulos

Presentation materials