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JACoW
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proceedings of accelator conferences held around the world
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| URL | https://doi.org/10.18429/JACoW-LINAC2024-TUPB006 |
|---|---|
| Title | Machine learning-based non-destructive measurement of bunch length at FRIB |
| Authors |
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| Abstract | A machine learning-based virtual diagnostic method for measuring the longitudinal phase space is proposed. Utilizing multiple measurements of bunch length from the Facility for Rare Isotope Beams (FRIB) accelerator, beam parameters are fitted with a concrete simulation model. A neural network model is trained to learn the correlations between the signals from beam position monitors (BPMs) and the bunch length. This model enables the rapid prediction of bunch length at BPM locations without compromising beam quality. |
| Paper | download: TUPB006.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 | 18 August 2024 |
| Revised | 28 August 2024 |
| Accepted | 28 August 2024 |
| Issued | 23 October 2024 |
| DOI | 10.18429/JACoW-LINAC2024-TUPB006 |
| Pages | 335-337 |
| 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. |