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| URL | https://doi.org/10.18429/JACoW-IBIC2024-THP60 |
|---|---|
| Title | Machine Learning Used in BPM Displacement Prediction at HLS II |
| Authors |
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| Abstract | Beam orbit stability is a crucial indicator that can be used to evaluate the performance of a synchrotron radiation source. It can be improved through precise orbit measurement with beam position monitors (BPMs) and appropriate orbit feedback. The move-ment of BPMs directly affects the measurement of the beam orbit and indirectly affects the beam orbit through orbit feedback (OFB) system. Two sets of BPM displacement measurement system were estab-lished at Hefei Light Source II (HLS II) storage ring and some machine learning work was carried out on the system. |
| Paper | download: THP60.pdf |
| Cite | BibTeX, LaTeX, Text/Word, RIS, EndNote |
| Conference | Proc. 13th International Beam Instrumentation Conference |
| Series | |
| Location | Beijing |
| Date | 09-13 Sep 2024 |
| Publisher | JACoW Publishing, Geneva, Switzerland |
| Editorial Board | Jianshe Cao (IHEP), Junhui Yue (IHEP), Lin Bian (IHEP), Ivan Andrian (Elettra-Sincrotrone Trieste S.C.p.A.), Volker RW Schaa (GSI), Zhichu Chen (SARI) |
| Online ISBN | 978-3-95450-249-3 |
| Online ISSN | 2673-5350 |
| Received | 05 September 2024 |
| Revised | 08 September 2024 |
| Accepted | 08 September 2024 |
| Issued | 11 December 2024 |
| DOI | 10.18429/JACoW-IBIC2024-THP60 |
| Pages | 612-615 |
| Copyright | Published by JACoW Publishing under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI. |