| Title |
Accelerating Linear Beam Dynamics Simulations for Machine Learning Applications |
| Authors |
- O. Stein, I.V. Agapovpresenter, A. Eichler, J. Kaiser
DESY, Hamburg, Germany
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| Abstract |
Machine learning has proven to be a powerful tool with many applications in the field of accelerator physics. Training machine learning models is a highly iterative process that requires large numbers of samples. However, beam time is often limited and many of the available simulation frameworks are not optimized for fast computation. As a result, training complex models can be infeasible. In this contribution, we introduce Cheetah, a linear beam dynamics framework optimized for fast computations. We show that Cheetah outperforms existing simulation codes in terms of speed and furthermore demonstrate the application of Cheetah to a reinforcement-learning problem as well as the successful transfer of the Cheetah-trained model to the real world. We anticipate that Cheetah will allow for faster development of more capable machine learning solutions in the field, one day enabling the development of autonomous accelerators.
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| Paper |
download WEPOMS036.PDF [0.139 MB / 4 pages] |
| Cite |
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| Conference |
IPAC2022 |
| Series |
International Particle Accelerator Conference (13th) |
| Location |
Bangkok, Thailand |
| Date |
12-17 June 2022 |
| Publisher |
JACoW Publishing, Geneva, Switzerland |
| Editorial Board |
Frank Zimmermann (CERN, Meyrin, Switzerland); Hitoshi Tanaka (RIKEN, Hyogo, Japan); Porntip Sudmuang (SRLI, Nakhon, Thailand); Prapong Klysubun (SRLI, Nakhon, Thailand); Prapaiwan Sunwong (SRLI, Nakhon, Thailand); Thakonwat Chanwattana (SRLI, Nakhon, Thailand); Christine Petit-Jean-Genaz (CERN, Meyrin, Switzerland); Volker R.W. Schaa (GSI, Darmstadt, Germany) |
| Online ISBN |
978-3-95450-227-1 |
| Online ISSN |
2673-5490 |
| Received |
07 June 2022 |
| Revised |
14 June 2022 |
| Accepted |
01 July 2022 |
| Issue Date |
01 July 2022 |
| DOI |
doi:10.18429/JACoW-IPAC2022-WEPOMS036 |
| Pages |
2330-2333 |
| 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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