III.12 — Machine learning

Authors

  • Jochem Snuverink

DOI:

https://doi.org/10.23730/CYRSP-2024-003.2131

Abstract

Machine learning (ML) is a powerful new mathematical toolbox that allows for new possibilities in a broad variety of research areas. It enables machines to fulfill tasks without being explicitly programmed. There is no reason to believe why machine learning cannot be applied to particle accelerators. After a short introduction to machine learning, in this chapter the existing applications to particle accelerators are reviewed with examples on the PSI accelerator complex.

Downloads

Published

2024-11-19

Issue

Vol. 3 No. I–IV (2024): Proceedings of the Joint Universities Accelerator School (JUAS)—Courses and exercises

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this publication agree to the following terms:

  1. CERN retains copyright and publishes the work licensed under the Creative Commons Attribution License 4.0 that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this series.
  2. Authors are able to enter into separate, additional contractual arrangements for distribution of the published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this series.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).