SpringerLink
Log in

Machine Learning in Particle Physics

  • Conference paper
  • First Online:
Big Data Analytics in Astronomy, Science, and Engineering (BDA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14516))

Included in the following conference series:

  • 72 Accesses

Abstract

This note surveys developments in particle physics due to advances made in the fields of statistics, machine learning, and artificial intelligence. With the aid of examples and recent work, this article attempts to give a flavor of the effect of these advances on particle physics, including brief mention of cloud computing, classic machine learning techniques, statistics applications, new ML/AI techniques, reinforcement learning, and other advances. Suggestions are made regarding the future.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 94.15
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 70.61
Price includes VAT (Germany)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. https://www.phy.olemiss.edu/HEP/comp.html & parallel_comp.html

  2. Hara, T.: Belle II Computing Update: KEK, 2020 May GDB

    Google Scholar 

  3. Khan, F.: HTCondor at Fermilab: Fermilab (2023)

    Google Scholar 

  4. De, K., Klimentov, A.: Future Data-Intensive Experiment Computing ...: Computing in High Energy and Nuclear Physics (2023)

    Google Scholar 

  5. The ATLAS Collaboration: Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC: Phys. Lett. B716, 1–29 (2012)

    Google Scholar 

  6. The ATLAS Collaboration, Measurement of the properties of Higgs boson production at \(\sqrt{s} = 13\) TeV in the \(H\rightarrow \gamma \gamma \) channel using 139 \(fb^{-1}\) of \(pp\) collision data with the ATLAS experiment: EP 07 (2023). 088

    Google Scholar 

  7. Inami, K.: TOP counter for particle identification at the Belle II experiment. Nucl. Instrum. Methods Phys. Res. A 766, 5–8 (2014)

    Article  Google Scholar 

  8. Sandilya, S., Schwartz, A.: Kaon and Pion Identification Performances in Phase III data for TOP detector: BELLE2-NOTE-PL-2019-014

    Google Scholar 

  9. Bessner, M.: The Belle II imaging Time-Of-Propagation (iTOP) detector in first collisions: VCI Vienna, b 22 (2019)

    Google Scholar 

  10. Image from Wikimedia Commons, Creative Commons license

    Google Scholar 

  11. Neyman, J., Pearson, E.S.: On the use and interpretation of certain test criteria for purposes of statistical inference. Biometrika 20A, 175–240, 263–294

    Google Scholar 

  12. Neyman, J., Pearson, E.S.: IX. On the problem of the most efficient tests of statistical hypotheses. Phil. Trans. R. Soc. Lond. A. 231(694–706), 289–337 (1933)

    Google Scholar 

  13. Mémoire sur la théorie des déblais et des remblais: G. Monge, De l’Imprimerie Royale (1781)

    Google Scholar 

  14. Kantorovich, L.V.: On the Translocation of Masses: J Math Sci 133, 1381-1382 (2006). Originally published in Dokl. Akad. Nauk SSSR, 37, No. 7–8, 227–229 (1942)

    Google Scholar 

  15. Brenier, Y.: Polar factorization and monotone rearrangement of vector-valued functions. Commun. Pure Appl. Math. 44(4), 375–417 (1991)

    Article  MathSciNet  Google Scholar 

  16. Villani, C.: Topics in Optimal Transportation: American Mathematical Society (2003). in the Graduate Studies in Mathematics Series

    Google Scholar 

  17. Figalli, A.: The Monge problem on non-compact manifolds: Rend. Sem. Mat. Univ. Padova 117, 147–166 (2007)

    Google Scholar 

  18. Vaserstein, L.N.: Markov processes over denumerable products of spaces, describing large systems of automata. Problemy Peredači Informacii. 5(3), 64–72 (1969)

    MathSciNet  Google Scholar 

  19. Rudin, C., et al.: Interpretable machine learning: fundamental principles and 10 grand challenges. Stat. Surv. 16, 1–85 (2022)

    Article  MathSciNet  Google Scholar 

  20. Alanazi, Y., et al.: A survey of machine learning-based physics event generation. In: The Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21) Survey Track

    Google Scholar 

  21. Butter, A., et al.: Ganplifying event samples. ar**v, 2008.06545 (2020)

    Google Scholar 

  22. Matchev, K.T., Shyamsundar, P.: Uncertainties associated with GAN-generated datasets in high energy physics: ar**V 2002.06307v2 (2020)

    Google Scholar 

  23. Kansal, R., et al.: Evaluating generative models in high energy physics. Phys. Rev. D 107, 076017 (2023)

    Article  Google Scholar 

  24. Matchev, K.T., Roman, A., Shyamsundar, P.: Uncertainties associated with GAN-generated datasets in high energy physics. SciPost Phys. 12(3), 104 (2022)

    Article  MathSciNet  Google Scholar 

  25. Butter, A., et al.: GANplifying Event Samples. ar**v:2008.06545 [hep-ph] (2022)

  26. “OxfordLanguages”. https://languages.oup.com/google-dictionary-en/

  27. The American Association of Physics Teachers (AAPT): Statement on Computational Physics (2023). https://www.aapt.org/Resources/policy/Statement-on-Computational-Physics.cfm

  28. Hardt, M., Recht, B.: Patterns, Predictions, and Actions. Princeton University Press, Princeton (2022)

    Google Scholar 

  29. Kasieczka, G., Nachman, B., Shih, D., et al.: The LHC Olympics 2020. Rep. Prog. Phys. 84, 12 IOP Publishing (2021). ar**v:2101.08320 [hep-ph]

Download references

Acknowledgments

Many thanks to the organizers Profs. Bhalla, Sachdeva, and Watanobe, and the crew of BASE23 at NIT Delhi, India and at the University of Aizu, Japan.

Author information

Authors and Affiliations

  1. Department of Physics and Astronomy, University of South Carolina, Columbia, SC, 29208, USA

    Milind V. Purohit

Authors
  1. Milind V. Purohit
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Milind V. Purohit .

Editor information

Editors and Affiliations

  1. National Institute of Technology Delhi, New Delhi, Delhi, India

    Shelly Sachdeva

  2. University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yutaka Watanobe

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Purohit, M.V. (2024). Machine Learning in Particle Physics. In: Sachdeva, S., Watanobe, Y. (eds) Big Data Analytics in Astronomy, Science, and Engineering. BDA 2023. Lecture Notes in Computer Science, vol 14516. Springer, Cham. https://doi.org/10.1007/978-3-031-58502-9_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-58502-9_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-58501-2

  • Online ISBN: 978-3-031-58502-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation