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| URL | https://doi.org/10.18429/JACoW-IPAC2025-THPS072 |
|---|---|
| Title | Real-time luminosity optimization in collider experiments using reinforcement learning |
| Authors |
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| Abstract | This study presents the development and implementation of a reinforcement learning-based algorithm for real-time luminosity tuning in collider experiments. The algorithm is initially pretrained on historical collider data and subsequently fine-tuned online during experiments. By analyzing accelerator measurements collected over several seconds, the model adjusts the magnetic structure to stabilize luminosity under varying experimental conditions. The proposed method allows for adaptive optimization without operator involvement, improving operational efficiency and stability. Results from its application on the VEPP-4M collider are presented, showcasing the method's feasibility and offering insights for its future development and application in accelerator systems. |
| Paper | download: THPS072.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 | 28 May 2025 |
| Revised | 03 June 2025 |
| Accepted | 04 June 2025 |
| Issued | 05 November 2025 |
| DOI | 10.18429/JACoW-IPAC2025-THPS072 |
| Pages | 3115-3116 |
| 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. |