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| URL | https://doi.org/10.18429/JACoW-LINAC2024-THXA004 |
|---|---|
| Title | Machine-learning-assisted beam tuning at FRIB |
| Authors |
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| Abstract | Facility for Rare Isotope Beams (FRIB) requires diverse primary ion species beams to produce rare isotopes. The beam tuning time can be reduced by employing Machine Learning (ML) techniques. In this presentation, we aim to explore practical perspectives on shortening beam tuning time. Specifically, we discuss customization of Bayesian Optimization for maximum beam time utilization, and virtual diagnostics that are currently under development. |
| Paper | download: THXA004.pdf |
| Cite | BibTeX, LaTeX, Text/Word, RIS, EndNote |
| Conference | 32nd Linear Accelerator Conference (LINAC2024) |
| Series | |
| Location | Chicago, IL, USA |
| Date | 25-30 Aug 2024 |
| Publisher | JACoW Publishing, Geneva, Switzerland |
| Editorial Board | Kelly Jaje (ANL), Kip Bishofberger (LANL), Ryan Brody (ANL), Gustavo Bruno (ANL), Jan Chrin (PSI), Cathy Eyberger (ANL), Maggie Montes (SLAC), Denise Skiadopoulos (ANL), Ling Wang (MSU/FRIB), Kent Wootton (ANL) |
| Online ISBN | 978-3-95450-219-6 |
| Online ISSN | 2226-0366 |
| Received | 20 August 2024 |
| Revised | 26 August 2024 |
| Accepted | 27 August 2024 |
| Issued | 23 October 2024 |
| DOI | 10.18429/JACoW-LINAC2024-THXA004 |
| Pages | 573-576 |
| 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. |