Generative Networks for LHC events

TL;DR

This paper explores the use of generative neural networks to improve the efficiency and capabilities of simulating and analyzing events at the Large Hadron Collider, potentially transforming LHC physics research.

Contribution

It introduces the application of generative networks for LHC event simulation and analysis, highlighting their potential to enhance existing tools and enable new analysis methods.

Findings

Generative networks can simulate LHC events efficiently.

Neural networks' invertibility offers new analysis opportunities.

Potential integration with existing LHC simulation frameworks.

Abstract

LHC physics crucially relies on our ability to simulate events efficiently from first principles. Modern machine learning, specifically generative networks, will help us tackle simulation challenges for the coming LHC runs. Such networks can be employed within established simulation tools or as part of a new framework. Since neural networks can be inverted, they also open new avenues in LHC analyses.