| Title |
Machine Learning Methods for Chromaticity Control at the 1.5 GeV Synchrotron Light Source DELTA |
| Authors |
- D. Schirmer, A. Althaus, T. Schüngel
DELTA, Dortmund, Germany
|
| Abstract |
In the past, the chromaticity values at the DELTA electron storage ring were manually adjusted using 15 individual sextupole power supply circuits, which are combined into 7 magnet families. To automate and optimize the time-consuming setting process, various machine learning (ML) approaches were investigated. For this purpose, simulations were first performed using a storage ring model and the performance of different neural network (NN) based models was compared. Subsequently, the neural networks were trained with experimental data and successfully implemented for chromaticity correction in real accelerator operation.
|
| Paper |
download TUPOPT059.PDF [0.515 MB / 4 pages] |
| Cite |
download ※ BibTeX
※ LaTeX
※ Text/Word
※ RIS
※ EndNote |
| 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 |
20 May 2022 |
| Revised |
11 June 2022 |
| Accepted |
15 June 2022 |
| Issue Date |
21 June 2022 |
| DOI |
doi:10.18429/JACoW-IPAC2022-TUPOPT059 |
| Pages |
1141-1144 |
| 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. |
|
