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| URL | https://doi.org/10.18429/JACoW-IPAC2025-TUPB032 |
|---|---|
| Title | Application of bayesian optimization in magnetic horn design |
| Authors |
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| Abstract | Bayesian optimization is an effective method for designing complex systems with costly, non-analytic black box objective functions. It enables efficient exploration of the parameter space, making it well-suited for challenging problems in accelerator design which involve computationally intensive simulations such as FLUKA. This study presents a framework to apply Bayesian optimization techniques to design the magnetic horn of Neutrinos from Stored Muons (nuSTORM) experiment for increased pion capture. The optimization process spans a wide range of operational energies, from 1 to 7 GeV, to address the physics reach of nuSTORM. Batch sampling is enabled through specialized acquisition functions, allowing simulations to run in parallel across a computational cluster and significantly reducing the time needed to identify optimal target and horn configurations for the muon source. By leveraging the surrogate models generated through Bayesian optimization, horn configurations at different energies are systematically compared. This facilitates sensitivity studies to determine a minimal set of horn designs that efficiently cover the nuSTORM kinematic range. |
| Paper | download: TUPB032.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 | 01 June 2025 |
| Accepted | 02 June 2025 |
| Issued | 05 November 2025 |
| DOI | 10.18429/JACoW-IPAC2025-TUPB032 |
| Pages | 1040-1043 |
| 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. |