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, Dorival Gonçalves, E. Gross, Theo Heimel, Gudrun Heinrich, L. Heinrich, A. Held, Stefan Höche, J. Howard, P. Ilten, Joshua Isaacson, Timo Janßen, Stephen Jones, M. Kado, M. Kagan, Gregor Kasieczka, Felix Kling, Sabine Kraml, Claudius Krause, Frank Krauss, Kevin Kröninger, Rahool Kumar Barman, Michel Luchmann, Vitaly Magerya, D. Maître, Bogdan Malaescu, Fabio Maltoni, Till Martini, Olivier Mattelaer, Benjamin Nachman, Sebastian Pitz, Juan Rojo, Matthew D. Schwartz, David Shih, F. Siegert, Roy Stegeman, Bob Stienen, Jesse Thaler, Rob Verheyen, D. Whiteson, Ramon Winterhalder, Jure Zupan
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.