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| URL | https://doi.org/10.18429/JACoW-NAPAC2025-MOP050 |
|---|---|
| Title | Improve beam brightness with bayesian optimization at the AGS booster injection at BNL |
| Authors |
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| Abstract | Alternating Gradient Synchrotron (AGS) and its Booster serve as part of the injector compound for RHIC and the future EIC at Brookhaven National Laboratory. Injection and early acceleration processes set maximum beam brightness for the collider rings. Such processes have many control parameters and are traditionally optimized empirically by operators. In an effort to streamline the injection processes with machine learning (ML) techniques, we develop and test a Bayesian Optimization (BO) algorithm to automatically tune the Linac to Booster (LtB) transfer line magnets to maximize beam brightness after injection into the Booster. We present experimental results that demonstrate BO can be applied to optimize Booster injection efficiency. |
| Paper | download: MOP050.pdf |
| Cite | BibTeX, LaTeX, Text/Word, RIS, EndNote |
| Conference | 6th North American Particle Accel. Conf. (NAPAC2025) |
| Series | |
| Location | Sacramento, CA, USA |
| Date | 10-15 Aug 2025 |
| Publisher | JACoW Publishing, Geneva, Switzerland |
| Editorial Board | Tor O. Raubenheimer (SLAC), Roark A. Marsh (LLNL), Eric Prebys (UC Davis), Ling Wang (MSU), Petr M. Anisimov (LANL), Kip Bishofberger (LANL), Gustavo Bruno (ANL), Zhichu Chen (SARI), Jan Chrin (PSI), Kelly Jaje (ANL), Jaeyu Lee (POSTECH), Magdalena Montes-Loera (SLAC), Mary Saethre (PHENOTYPE), Tasha Summers (SLAC), Kent Wootton (ANL) |
| Online ISBN | 978-3-95450-261-5 |
| Online ISSN | 2673-7000 |
| Received | 06 August 2025 |
| Revised | 10 August 2025 |
| Accepted | 10 August 2025 |
| Issued | 28 January 2026 |
| DOI | 10.18429/JACoW-NAPAC2025-MOP050 |
| Pages | 157-159 |
| 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. |