Contribution to a conference proceedings/Journal Article

PUBDB-2023-07799

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Machine learning and LHC event generation

Butter, A. ; Plehn, T. ; 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.

2023
SciPost Foundation Amsterdam

This record in other databases:        

Please use a persistent id in citations: doi:10.21468/SciPostPhys.14.4.079  doi:10.3204/PUBDB-2023-07799

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 ; interpretation of experiments: 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)
2. DFG project G:(GEPRIS)390833306 - EXC 2121: Quantum Universe (390833306) (390833306)
3. DFG project G:(GEPRIS)390900948 - EXC 2181: STRUKTUREN: Emergenz in Natur, Mathematik und komplexen Daten (390900948) (390900948)

Experiment(s):

1. No specific instrument

Appears in the scientific report 2023

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Database coverage:
; ; ; ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; DOAJ Seal ; IF >= 5 ; JCR ; SCOPUS ; Web of Science Core Collection

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Linked articles:

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Preprint Butter, A. ; Plehn, T. (Corresponding author) ; Schumann, S. ; et al
Machine Learning and LHC Event Generation
[10.3204/PUBDB-2022-01431]     Files  Fulltext by arXiv.org BibTeX | EndNote: XML, Text | RIS

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