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Title: Machine learning and LHC event generation

Journal Article · · SciPost Physics
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  1. Heidelberg Institute for Theoretical Studies, Sorbonne University
  2. Heidelberg Institute for Theoretical Studies
  3. University of Göttingen
  4. University of Turin
  5. National Institute for Subatomic Physics, Radboud University Nijmegen
  6. New York University
  7. Sapienza University of Rome
  8. Weizmann Institute of Science
  9. University of Milan
  10. University of Tokyo
  11. Oklahoma State University
  12. Karlsruhe Institute of Technology
  13. Technical University of Munich
  14. Fermi National Accelerator Laboratory
  15. University of California, Irvine
  16. University of Cincinnati
  17. Durham University
  18. University of Paris-Saclay, Sapienza University of Rome
  19. SLAC National Accelerator Laboratory
  20. University of Hamburg
  21. Deutsche Elektronen-Synchrotron DESY
  22. French National Centre for Scientific Research
  23. Rutgers University
  24. TU Dortmund University
  25. Sorbonne University
  26. Université catholique de Louvain, University of Bologna
  27. Humboldt University of Berlin
  28. Université catholique de Louvain
  29. Lawrence Berkeley National Laboratory, University of California, Berkeley
  30. National Institute for Subatomic Physics, VU University Amsterdam
  31. Harvard University
  32. Dresden University of Technology
  33. Radboud University Nijmegen
  34. Massachusetts Institute of Technology
  35. University College London
+ Show Author Affiliations

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.

Research Organization:
Cincinnati U.; DESY; Dortmund U.; Dresden, Tech. U.; Durham U., IPPP; Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States); Gottingen U.; Hamburg U.; Harvard U.; Humboldt U., Berlin; IJCLab, Orsay; INFN, Milan; INFN, Rome; INFN, Turin; KIT, Karlsruhe, TP; LBNL, Berkeley; LPSC, Grenoble; Louvain U., CP3; MIT, Cambridge, CTP; Milan U.; Munich, Tech. U.; New York U.; New York U. (main); New York U., CCPP; Nijmegen U., IMAPP; Nikhef, Amsterdam; Oklahoma State U.; Paris U., VI-VII; Rome U.; Rutgers U., Piscataway; SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Tokyo U., ICEPP; Turin U.; U. Bologna, DIFA; U. Coll. London; U. Heidelberg, ITP; UC, Berkeley (main); UC, Irvine; Vrije U., Amsterdam; Weizmann Inst.
Sponsoring Organization:
US Department of Energy; USDOE
Grant/Contract Number:
AC02-05CH11231; AC02-07CH11359; AC02-76SF00515; SC0016013
OSTI ID:
1971144
Report Number(s):
FERMILAB-PUB-22-126-T; 079
Journal Information:
SciPost Physics, Journal Name: SciPost Physics Journal Issue: 4 Vol. 14; ISSN 2542-4653
Publisher:
Stichting SciPostCopyright Statement
Country of Publication:
Netherlands
Language:
English

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  • Alanazi, Yasir; Sato, Nobuo; Liu, Tianbo
  • Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}, Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence https://doi.org/10.24963/ijcai.2021/293
conference August 2021
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Related Subjects

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS
LHC
ML
Monte-Carlo simulations
large hadron collider
machine learning