Preprint

PUBDB-2022-01431

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Machine Learning and LHC Event Generation

Butter, A. ; Plehn, T. (Corresponding author) ; Schumann, S. ; Badger, S. ; Caron, S. ; Cranmer, K. ; Di Bello, F. A. ; Dreyer, E. ; Forte, S. ; Ganguly, S. ; Gonçalves, D. ; Gross, E. ; Heimel, T. ; Heinrich, G. ; Heinrich, L. ; Held, A. ; Höche, S. ; Howard, J. N. ; Ilten, P. ; Isaacson, J. ; Janßen, T. ; Jones, S. ; Kado, M. ; Kagan, M. ; Kasieczka, G.Extern* ; Kling, F.DESY* ; Kraml, S. ; Krause, C. ; Krauss, F. ; Kröninger, K. ; Barman, R. K. ; Luchmann, M. ; Magerya, V.Extern* ; Maitre, D. ; Malaescu, B. ; Maltoni, F. ; Martini, T. ; Mattelaer, O. ; Nachman, B. ; Pitz, S. ; Rojo, J. ; Schwartz, M. ; Shih, D. ; Siegert, F.Extern* ; Stegeman, R. ; Stienen, B. ; Thaler, J. ; Verheyen, R. ; Whiteson, D. ; Winterhalder, R. ; Zupan, J.

2022

This record in other databases:  

Please use a persistent id in citations: doi:10.3204/PUBDB-2022-01431

Report No.: DESY-22-043; arXiv:2203.07460

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.

Keyword(s): interface ; CERN LHC Coll

Note: Contribution to Snowmass 2021

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Contributing Institute(s):

1. Theorie-Gruppe (T)

Research Program(s):

1. 611 - Fundamental Particles and Forces (POF4-611) (POF4-611)

Experiment(s):

1. No specific instrument

Appears in the scientific report 2022

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Database coverage:
; Published

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Linked articles:

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Contribution to a conference proceedings/Journal Article Butter, A. ; Plehn, T. ; Schumann, S. ; et al
Machine learning and LHC event generation
2023Snowmass 2021, SeattleSeattle, United States, 17 Jul 2022 - 26 Jul 20222022-07-172022-07-26 SciPost physics 14(4), 079 (2023) [10.21468/SciPostPhys.14.4.079]2023     Files BibTeX | EndNote: XML, Text | RIS

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