TY  - JOUR
AU  - Butter, Anja
AU  - Plehn, Tilman
AU  - Schumann, Steffen
AU  - Badger, Simon
AU  - Caron, Sascha
AU  - Cranmer, Kyle
AU  - Di Bello, Francesco Armando
AU  - Dreyer, Etienne
AU  - Forte, Stefano
AU  - Ganguly, Sanmay
AU  - Gonçalves, Dorival
AU  - Gross, Eilam
AU  - Heimel, Theo
AU  - Heinrich, Gudrun
AU  - Heinrich, Lukas
AU  - Held, Alexander
AU  - Höche, Stefan
AU  - Howard, Jessica N.
AU  - Ilten, Philip
AU  - Isaacson, Joshua
AU  - Janßen, Timo
AU  - Jones, Stephen
AU  - Kado, Marumi
AU  - Kagan, Michael
AU  - Kasieczka, Gregor
AU  - Kling, Felix
AU  - Kraml, Sabine
AU  - Krause, Claudius
AU  - Krauss, Frank
AU  - Kröninger, Kevin
AU  - Barman, Rahool Kumar
AU  - Luchmann, Michel
AU  - Magerya, Vitaly
AU  - Maitre, Daniel
AU  - Malaescu, Bogdan
AU  - Maltoni, Fabio
AU  - Martini, Till
AU  - Mattelaer, Olivier
AU  - Nachman, Benjamin
AU  - Pitz, Sebastian
AU  - Rojo, Juan
AU  - Schwartz, Matthew
AU  - Shih, David
AU  - Siegert, Frank
AU  - Stegeman, Roy
AU  - Stienen, Bob
AU  - Thaler, Jesse
AU  - Verheyen, Rob
AU  - Whiteson, Daniel
AU  - Winterhalder, Ramon
AU  - Zupan, Jure
TI  - Machine learning and LHC event generation
JO  - SciPost physics
VL  - 14
IS  - 4
SN  - 2542-4653
CY  - Amsterdam
PB  - SciPost Foundation
M1  - PUBDB-2023-07799
SP  - 079
PY  - 2023
N1  - Contribution to: Snowmass 2021
AB  - 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.
T2  - Snowmass 2021
CY  - 17 Jul 2022 - 26 Jul 2022, Seattle (United States)
Y2  - 17 Jul 2022 - 26 Jul 2022
M2  - Seattle, United States
KW  - interface (INSPIRE)
KW  - interpretation of experiments: CERN LHC Coll (INSPIRE)
LB  - PUB:(DE-HGF)8 ; PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000996367300018
DO  - DOI:10.21468/SciPostPhys.14.4.079
UR  - https://bib-pubdb1.desy.de/record/600225
ER  -