| Title |
Summary Report on Machine Learning-Based Applications at the Synchrotron Light Source Delta |
| Authors |
- D. Schirmer, S. Khan, A. Radha Krishnan
DELTA, Dortmund, Germany
|
| Abstract |
In recent years, several control system applications using machine learning (ML) techniques have been developed and tested to automate the control and optimization of the 1.5 GeV synchrotron radiation source DELTA. These applications cover a wide range of tasks, including electron beam position correction, working point control, chromaticity adjustment, injection process optimization, as well as CHG-spectra (coherent harmonic generation) analysis. Various machine learning techniques have been used to implement these projects. This report provides an overview of these projects, summarizes the current results, and indicates ideas for future improvements.
|
| Paper |
download TUPDP020.PDF [2.523 MB / 5 pages] |
| Cite |
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※ EndNote |
| Conference |
ICALEPCS2023 |
| Series |
International Conference on Accelerator and Large Experimental Physics Control Systems (19th) |
| Location |
Cape Town, South Africa |
| Date |
09-13 October 2023 |
| Publisher |
JACoW Publishing, Geneva, Switzerland |
| Editorial Board |
Volker RW Schaa (GSI, Darmstadt, Germany); Andy Götz (ESRF, Grenoble, France); Johan Venter (SARAO, Cape Town, South Africa); Karen White (SNS, Oak Ridge, TN, USA); Marie Robichon (ESRF, Grenoble, France); Vivienne Rowland (SARAO, Cape Town, South Africa) |
| Online ISBN |
978-3-95450-238-7 |
| Online ISSN |
2226-0358 |
| Received |
04 October 2023 |
| Accepted |
06 December 2023 |
| Issued/td>
| 13 December 2023 |
| DOI |
doi:10.18429/JACoW-ICALEPCS2023-TUPDP020 |
| Pages |
537-541 |
| 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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