Machine learning and LHC event generation-Reference-Cited by-同舟云学术

Machine learning and LHC event generation

Published:2023-04-21 Issue:4 Volume:14 Page:
ISSN:2542-4653
Container-title:SciPost Physics
language:
Short-container-title:SciPost Phys.

Author:

Butter Anja¹²,Plehn Tilman¹,Schumann Steffen³,Badger Simon⁴,Caron Sascha⁵⁶,Cranmer Kyle⁷,Di Bello Francesco Armando⁸,Dreyer Etienne⁹,Forte Stefano¹⁰,Ganguly Sanmay¹¹,Gonçalves Dorival¹²,Gross Eilam⁹,Heimel Theo¹,Heinrich Gudrun¹³,Heinrich Lukas¹⁴,Held Alexander⁷,Höche Stefan¹⁵,Howard Jessica N.¹⁶,Ilten Philip¹⁷,Isaacson Joshua¹⁵,Janßen Timo³,Jones Stephen¹⁸,Kado Marumi¹⁹⁸,Kagan Michael²⁰,Kasieczka Gregor²¹,Kling Felix²²,Kraml Sabine²³,Krause Claudius²⁴,Krauss Frank¹⁸,Kröninger Kevin²⁵,Barman Rahool Kumar¹²,Luchmann Michel¹,Magerya Vitaly¹³,Maitre Daniel¹⁸,Malaescu Bogdan²,Maltoni Fabio²⁶²⁷,Martini Till²⁸,Mattelaer Olivier²⁶,Nachman Benjamin²⁹³⁰,Pitz Sebastian¹,Rojo Juan⁵³¹,Schwartz Matthew³²,Shih David²³,Siegert Frank³³,Stegeman Roy¹⁰,Stienen Bob⁶,Thaler Jesse³⁴,Verheyen Rob³⁵,Whiteson Daniel¹⁶,Winterhalder Ramon²⁶,Zupan Jure¹⁷

Affiliation:

1. Heidelberg Institute for Theoretical Studies

2. Sorbonne University

3. University of Göttingen

4. University of Turin

5. National Institute for Subatomic Physics

6. Radboud University Nijmegen

7. New York University

8. Sapienza University of Rome

9. Weizmann Institute of Science

10. University of Milan

11. University of Tokyo

12. Oklahoma State University

13. Karlsruhe Institute of Technology

14. Technical University of Munich

15. Fermi National Accelerator Laboratory

16. University of California, Irvine

17. University of Cincinnati

18. Durham University

19. University of Paris-Saclay

20. SLAC National Accelerator Laboratory

21. University of Hamburg

22. Deutsche Elektronen-Synchrotron DESY

23. French National Centre for Scientific Research

24. Rutgers University

25. TU Dortmund University

26. Université catholique de Louvain

27. University of Bologna

28. Humboldt University of Berlin

29. Lawrence Berkeley National Laboratory

30. University of California, Berkeley

31. VU University Amsterdam

32. Harvard University

33. Dresden University of Technology

34. Massachusetts Institute of Technology

35. University College London

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.

Funder

Agence Nationale de la Recherche

Bundesministerium für Bildung und Forschung

Deutsche Forschungsgemeinschaft

European Research Council

Fonds De La Recherche Scientifique - FNRS

Institut National de Physique Nucléaire et de Physique des Particules

National Science Foundation

United States Department of Energy

Publisher

Stichting SciPost

Subject