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proceedings of accelerator conferences held around the world
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| URL | https://doi.org/10.18429/JACoW-IPAC2025-THPM026 |
|---|---|
| Title | Application of Bayesian optimization for the TLS booster extraction |
| Authors |
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| Abstract | Bayesian optimization is a method for performing global optimization on black-box functions using Gaussian processes and an acquisition function. In accelerator parameter tuning, when the number of adjustable parameters is large, finding the global optimal parameters can be time-consuming and often relies on the operator’s experience. Bayesian optimization is well-suited for such scenarios. In this report, we take the booster extraction of the Taiwan Light Source (TLS) as an example, selecting six key adjustable parameters to optimize the extraction efficiency from the booster ring to the transport line. The preliminary test results and implementation details will be discussed in this paper. |
| Paper | download: THPM026.pdf |
| Cite | BibTeX, LaTeX, Text/Word, RIS, EndNote |
| Conference | 16th International Particle Accelerator Conference |
| Series | |
| Location | Taipei, Taiwan |
| Date | 01-06 Jun 2025 |
| Publisher | JACoW Publishing, Geneva, Switzerland |
| Editorial Board | * Ming-Chyuan Lin - National Synchrotron Radiation Research Center * Yoichi Sato - KEK * Jui-Che Huang - National Synchrotron Radiation Research Center * David Button - Australian Nuclear Science and Technology Organisation * Ping-Shun Chuang - National Synchrotron Radiation Research Center |
| Online ISBN | 978-3-95450-248-6 |
| Online ISSN | 2673-5490 |
| Received | 08 May 2025 |
| Revised | 14 October 2025 |
| Accepted | 14 October 2025 |
| Issued | 05 November 2025 |
| DOI | 10.18429/JACoW-IPAC2025-THPM026 |
| Pages | 2740-2742 |
| 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. |