%0 Journal Article
%A Butter, Anja
%A Plehn, Tilman
%A Schumann, Steffen
%A Badger, Simon
%A Caron, Sascha
%A Cranmer, Kyle
%A Di Bello, Francesco Armando
%A Dreyer, Etienne
%A Forte, Stefano
%A Ganguly, Sanmay
%A Gonçalves, Dorival
%A Gross, Eilam
%A Heimel, Theo
%A Heinrich, Gudrun
%A Heinrich, Lukas
%A Held, Alexander
%A Höche, Stefan
%A Howard, Jessica N.
%A Ilten, Philip
%A Isaacson, Joshua
%A Janßen, Timo
%A Jones, Stephen
%A Kado, Marumi
%A Kagan, Michael
%A Kasieczka, Gregor
%A Kling, Felix
%A Kraml, Sabine
%A Krause, Claudius
%A Krauss, Frank
%A Kröninger, Kevin
%A Barman, Rahool Kumar
%A Luchmann, Michel
%A Magerya, Vitaly
%A Maitre, Daniel
%A Malaescu, Bogdan
%A Maltoni, Fabio
%A Martini, Till
%A Mattelaer, Olivier
%A Nachman, Benjamin
%A Pitz, Sebastian
%A Rojo, Juan
%A Schwartz, Matthew
%A Shih, David
%A Siegert, Frank
%A Stegeman, Roy
%A Stienen, Bob
%A Thaler, Jesse
%A Verheyen, Rob
%A Whiteson, Daniel
%A Winterhalder, Ramon
%A Zupan, Jure
%T Machine learning and LHC event generation
%J SciPost physics
%V 14
%N 4
%@ 2542-4653
%C Amsterdam
%I SciPost Foundation
%M PUBDB-2023-07799
%P 079
%D 2023
%Z Contribution to: Snowmass 2021
%X 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.
%B Snowmass 2021
%C 17 Jul 2022 - 26 Jul 2022, Seattle (United States)
Y2 17 Jul 2022 - 26 Jul 2022
M2 Seattle, United States
%K interface (INSPIRE)
%K interpretation of experiments: CERN LHC Coll (INSPIRE)
%F PUB:(DE-HGF)8 ; PUB:(DE-HGF)16
%9 Contribution to a conference proceedingsJournal Article
%U <Go to ISI:>//WOS:000996367300018
%R 10.21468/SciPostPhys.14.4.079
%U https://bib-pubdb1.desy.de/record/600225