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

TL;DR

This paper explores the use of Particle GANs to generate realistic LHC collision events, including pileup, by modeling particle four-momenta, aiming to improve simulation accuracy and efficiency.

Contribution

It introduces a novel GAN-based framework for full-event particle generation at the LHC, capable of modeling complex detector geometries and pileup conditions.

Findings

GAN can generate realistic LHC collision events

Conditional GAN effectively models missing transverse energy

Method improves simulation speed and realism

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.