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Machine Learning and LHC Event Generation
by Anja Butter, Tilman Plehn, Steffen Schumann, Simon Badger, Sascha Caron, Kyle Cranmer, Francesco Armando Di Bello, Etienne Dreyer, Stefano Forte, Sanmay Ganguly, Dorival Gonçalves, Eilam Gross, Theo Heimel, Gudrun Heinrich, Lukas Heinrich, Alexander Held, Stefan Höche, Jessica N. Howard, Philip Ilten, Joshua Isaacson, Timo Janßen, Stephen Jones, Marumi Kado, Michael Kagan, Gregor Kasieczka, Felix Kling, Sabine Kraml, Claudius Krause, Frank Krauss, Kevin Kröninger, Rahool Kumar Barman, Michel Luchmann, Vitaly Magerya, Daniel Maitre, Bogdan Malaescu, Fabio Maltoni, Till Martini, Olivier Mattelaer, Benjamin Nachman, Sebastian Pitz, Juan Rojo, Matthew Schwartz, David Shih, Frank Siegert, Roy Stegeman, Bob Stienen, Jesse Thaler, Rob Verheyen, Daniel Whiteson, Ramon Winterhalder, Jure Zupan
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Submission summary
| Authors (as registered SciPost users): | Stefan Höche · Joshua Isaacson · Timo Janßen · Sabine Kraml · Claudius Krause · Frank Krauss · Olivier Mattelaer · Tilman Plehn · Steffen Schumann · Frank Siegert · Rob Verheyen · Ramon Winterhalder · Jure Zupan |
| Submission information | |
|---|---|
| Preprint Link: | https://arxiv.org/abs/2203.07460v2 (pdf) |
| Date accepted: | 2023-02-09 |
| Date submitted: | 2023-01-09 17:24 |
| Submitted by: | Plehn, Tilman |
| Submitted to: | SciPost Physics |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
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| Approach: | Phenomenological |
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.
List of changes
We would like to thank the referee and accommodated all his/her requests. For the non-ML aspects of event generators we added the corresponding Snowmass-inspired review as Ref.[6].
Published as SciPost Phys. 14, 079 (2023)