Machine Learning and LHC Event Generation

Abstract

First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements of particle physics. New ideas and tools developed at the interface of particle physics and machine learning will improve the speed and precision of forward simulations, handle the complexity of collision data, and enhance inference as an inverse simulation problem.

Review article based on a Snowmass 2021 contribution

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• Event Generators for High-Energy Physics Experiments
• Fast Point Cloud Generation with Diffusion Models in High Energy Physics
• EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets
• MadNIS -- Neural Multi-Channel Importance Sampling
• How to Understand Limitations of Generative Networks
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