Machine learning and LHC event generation

Butter, Anja; Plehn, Tilman; Schumann, Steffen; Badger, Simon; Caron, Sascha; Cranmer, Kyle; Di Bello, Francesco Armando; Dreyer, Etienne; Forte, Stefano; Ganguly, Sanmay; Gonçalves, Dorival; Gross, Eilam; Heimel, Theo; Heinrich, Gudrun; Heinrich, Lukas; Held, Alexander; Höche, Stefan; Howard, Jessica N.; Ilten, Philip; Isaacson, Joshua; Janßen, Timo; Jones, Stephen; Kado, Marumi; Kagan, Michael; Kasieczka, Gregor; Kling, Felix; Kraml, Sabine; Krause, Claudius; Krauss, Frank; Kröninger, Kevin; Barman, Rahool Kumar; Luchmann, Michel; Magerya, Vitaly; Maitre, Daniel; Malaescu, Bogdan; Maltoni, Fabio; Martini, Till; Mattelaer, Olivier; Nachman, Benjamin; Pitz, Sebastian; Rojo, Juan; Schwartz, Matthew; Shih, David; Siegert, Frank; Stegeman, Roy; Stienen, Bob; Thaler, Jesse; Verheyen, Rob; Whiteson, Daniel; Winterhalder, Ramon; Zupan, Jure

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.

Citation

Butter, A., Plehn, T., Schumann, S., Badger, S., Caron, S., Cranmer, K., …Zupan, J. (2023). Machine learning and LHC event generation. SciPost Physics, 14(4), Article 079. https://doi.org/10.21468/scipostphys.14.4.079

• Journal Article Type: Article
• Acceptance Date: Feb 9, 2023
• Online Publication Date: Apr 21, 2023
• Publication Date: Apr 21, 2023
• Deposit Date: Feb 19, 2024
• Publicly Available Date: Feb 19, 2024
• Journal: SciPost Physics
• Print ISSN: 2542-4653
• Publisher: SciPost
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 4
• Article Number: 079
• DOI: https://doi.org/10.21468/scipostphys.14.4.079
• Keywords: General Physics and Astronomy
• Public URL: https://durham-repository.worktribe.com/output/2269736