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proceedings of accelator conferences held around the world
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| URL | https://doi.org/10.18429/JACoW-LINAC2024-MOAA012 |
|---|---|
| Title | Automation of RF tuning for medical accelerators |
| Authors |
|
| Abstract | RadiaSoft is developing machine learning methods to improve the operation and control of industrial accelerators. Because industrial systems typically suffer from a lack of instrumentation and a noisier environment, advancements in control methods are critical for optimizing their performance. In particular, our recent work has focused on the development of pulse-to-pulse feedback algorithms for use in dose optimization for FLASH radiotherapy. The PHASER (pluridirectional high-energy agile scanning electronic radiotherapy) system is of particular interest due to the need to synchronize 16 different accelerators all with their own noise characteristics. This presentation will provide an overview of the challenges associated with RF tuning for a PHASER-like system, a description of the model used to evaluate different control schema, and our initial results using conventional methods and machine learning methods. |
| Paper | download: MOAA012.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 | 19 August 2024 |
| Revised | 27 August 2024 |
| Accepted | 27 August 2024 |
| Issued | 23 October 2024 |
| DOI | 10.18429/JACoW-LINAC2024-MOAA012 |
| Pages | 47-49 |
| 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. |