| Title |
Adding Machine Learning to the Analysis and Optimization Toolsets at the Light Source BESSY II |
| Authors |
- L. Vera Ramirez, T. Mertens, R. Müller, J. Viefhaus
HZB, Berlin, Germany
- G. Hartmann
University of Kassel, Kassel, Germany
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| Abstract |
The Helmholtz Association has initiated the implementation of the Data Management and Analysis concept across its centers in Germany. At Helmholtz-Zentrum Berlin, both the beamline and the machine (accelerator) groups have started working towards setting up the infrastructure and tools to introduce modern analysis, optimization, automation and AI techniques for improving the performance of the (large scale) user facility and its experimental setups. This paper focuses on our first steps with Machine Learning techniques over the past months at BESSY II as well as organizational topics and collaborations. The presented results correspond to two complementary scenarios. The first one is based on supervised ML models trained with real accelerator data, whose target are real-time predictions for several measurements (lifetime, efficiency, beam loss, …); some of these techniques are also used for additional tasks such as outlier detection or feature importance analysis. The second scenario includes first prototypes towards self-tuning of machine parameters in different optimization cases (injection efficiency, orbit correction, …) with Deep Reinforcement Learning agents.
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| Paper |
download TUCPL01.PDF [2.836 MB / 7 pages] |
| Slides |
download TUCPL01_TALK.PDF [8.894 MB] |
| Export |
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| Conference |
ICALEPCS2019 |
| Series |
International Conference on Accelerator and Large Experimental Physics Control Systems (17th) |
| Location |
New York, NY, USA |
| Date |
05-11 October 2019 |
| Publisher |
JACoW Publishing, Geneva, Switzerland |
| Editorial Board |
Karen S. White (ORNL, Oak Ridge, TN, USA); Kevin A. Brown (BNL, Upton, NY, USA); Philip S. Dyer (BNL, Upton, NY, USA); Volker RW Schaa (GSI, Darmstadt, Germany) |
| Online ISBN |
978-3-95450-209-7 |
| Online ISSN |
2226-0358 |
| Received |
27 September 2019 |
| Accepted |
10 October 2019 |
| Issue Date |
30 August 2020 |
| DOI |
doi:10.18429/JACoW-ICALEPCS2019-TUCPL01 |
| Pages |
754-760 |
| 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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