| Title |
Continuous data-driven control of the GTS-LHC ion source at CERN |
| Authors |
- V. Kain, B. Rodriguez Mateos, N. Bruchon, D. Küchlerpresenter
CERN, Geneva, Switzerland
- S. Hirlaender
University of Salzburg, Salzburg, Austria
|
| Abstract |
Recent advances with the CERN infrastructure for machine learning allows to deploy state-of-the-art data-driven control algorithms for stabilising and optimising particle accelerator systems. This contribution summarises the results of the first tests with different continuous control algorithms to optimise the intensity out of the CERN LINAC3 source. The task is particularly challenging due to the different latencies for control parameters that range from instantaneous response, to full response after only ~30 minutes. The next steps and a vision towards full deployment and autonomous source control will also be discussed.
|
| Paper |
download MOP11.PDF [0.492 MB / 4 pages] |
| Cite |
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※ EndNote |
| Conference |
ECRIS2024 |
| Series |
International Workshop on Electron Cyclotron Resonance Ion Sources (26th) |
| Location |
Darmstadt, Germany |
| Date |
15-19 September 2024 |
| Publisher |
JACoW Publishing, Geneva, Switzerland |
| Editorial Board |
Fabio Maimone (GSI, Darmstadt, Germany); Volker RW Schaa (GSI, Darmstadt, Germany); Raphael Mueller (GSI, Darmstadt, Germany) |
| Online ISBN |
978-3-95450-257-8 |
| Online ISSN |
2222-5692 |
| Received |
14 September 2024 |
| Revised |
17 September 2024 |
| Accepted |
29 January 2025 |
| Issued |
18 May 2025 |
| DOI |
doi:10.18429/JACoW-ECRIS2024-MOP11 |
| Pages |
56-59 |
| Copyright |
 Published by JACoW Publishing under the terms of the Creative Commons Attribution 4.0 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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