Details

Autor(en) / Beteiligte

• 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

Titel

Machine learning and LHC event generation

Ist Teil von

• SciPost physics, 2023-04, Vol.14 (4), p.079, Article 079

Ort / Verlag

Netherlands: Stichting SciPost

Erscheinungsjahr

2023

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• 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.