Machine learning and LHC event generation

Anja Butter, Tilman Plehn, S. Schumann, Simon Badger, S. Caron, K. Cranmer, F. A. Di Bello, E. Dreyer, Stefano Forte, Sanmay Ganguly

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.

Locations

• SciPost Physics - View - PDF
• arXiv (Cornell University) - View - PDF
• Radboud Repository (Radboud University) - View - PDF
• Desy publication database (The Deutsches Elektronen-Synchrotron) - View - PDF
• OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) - View
• HAL (Le Centre pour la Communication Scientifique Directe) - View