| Title |
Machine Learning Techniques for Optics Measurements and Corrections |
| Authors |
- E. Fol, R. Tomás García
CERN, Meyrin, Switzerland
- G. Franchetti
GSI, Darmstadt, Germany
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| Abstract |
Recently, various efforts have presented Machine Learning (ML) as a powerful tool for solving accelerator problems. In the LHC a decision tree-based algorithm has been applied to detect erroneous beam position monitors demonstrating successful results in operation. Supervised regression models trained on simulations of LHC optics with quadrupole errors promise to significantly speed-up optics corrections by finding local errors in the interaction regions. The implementation details, results and future plans for these studies will be discussed following a brief introduction to ML concepts and its suitability to different problems in the domain of accelerator physics.
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| Paper |
download WEVIR12.PDF [0.374 MB / 6 pages] |
| Export |
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| Conference |
IPAC2020 |
| Series |
International Particle Accelerator Conference (11th) |
| Location |
Caen, France |
| Date |
10-15 May 2020 |
| Publisher |
JACoW Publishing, Geneva, Switzerland |
| Editorial Board |
Mike Seidel (PSI, Villigen, Switzerland); Ralph W. Aßmann (DESY, Hamburg, Germany); Frédéric Chautard (GANIL, Caen. France); Volker R.W. Schaa (GSI, Darmstadt, Germany) |
| Online ISBN |
978-3-95450-213-4 |
| Online ISSN |
2673-5490 |
| Received |
02 June 2020 |
| Accepted |
12 June 2020 |
| Issue Date |
10 October 2020 |
| DOI |
doi:10.18429/JACoW-IPAC2020-WEVIR12 |
| Pages |
61-66 |
| Copyright |
 Published by JACoW Publishing under the terms of the Creative Commons Attribution 3.0 International license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI. |
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