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| URL | https://doi.org/10.18429/JACoW-ICALEPCS2025-WEPD078 |
|---|---|
| Title | Machine learning for ISIS Controls |
| Authors |
|
| Abstract | Machine learning methods have been demonstrated significant promise when applied in the context of accelerator controls. This work highlights the ongoing efforts using ML methods to improve and optimise the operation of the accelerator. We present current applications in anomaly detection, optimisation and digital twinning. As well as control applications the machine learning operations (MLOps) work required for rapid prototyping and reliable and repeatable deployment is presented. Finally, we discuss the current and future impact of ML onthe ISIS Control system. |
| Paper | download: WEPD078.pdf |
| Cite | BibTeX, LaTeX, Text/Word, RIS, EndNote |
| Conference | 20th Int. Conf. Accel. Large Exp. Phys. Control Syst. (ICALEPCS'25) |
| Series | |
| Location | Chicago, IL, USA |
| Date | 20-26 Sep 2025 |
| Publisher | JACoW Publishing, Geneva, Switzerland |
| Editorial Board | Kent Wootton (ANL), Jeffery Biss (FNAL), Gustavo Bruno (ANL), Jeff Kirchman (ANL), Kelly Jaje (ANL), Denise Skiadopoulos (ANL), SuYin Grass Wang (ANL) |
| Online ISBN | 978-3-95450-255-4 |
| Online ISSN | 2226-0358 |
| Received | 06 September 2025 |
| Revised | 29 September 2025 |
| Accepted | 31 October 2025 |
| Issued | 25 November 2025 |
| DOI | 10.18429/JACoW-ICALEPCS2025-WEPD078 |
| Pages | 1227-1232 |
| 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. |