ICALEPCS2023 - List of Keywords (industrial-accelerators)

Paper	Title	Other Keywords	Page
TUPDP115	Machine Learning for Compact Industrial Accelerators	cavity, controls, simulation, network	846
	J.P. Edelen, J.A. Einstein-Curtis, M.J. Henderson, M.C. Kilpatrick RadiaSoft LLC, Boulder, Colorado, USA J.A. Diaz Cruz, A.L. Edelen SLAC, Menlo Park, California, USA
	Funding: This material is based upon work supported by the DOE Accelerator R&D and Production under Award Number DE-SC0023641. The industrial and medical accelerator industry is an ever-growing field with advancements in accelerator technology enabling its adoption for new applications. As the complexity of industrial accelerators grows so does the need for more sophisticated control systems to regulate their operation. Moreover, the environment for industrial and medical accelerators is often harsh and noisy as opposed to the more controlled environment of a laboratory-based machine. This environment makes control more challenging. Additionally, instrumentation for industrial accelerators is limited making it difficult at times to identify and diagnose problems when they occur. RadiaSoft has partnered with SLAC to develop new machine learning methods for control and anomaly detection for industrial accelerators. Our approach is to develop our methods using simulation models followed by testing on experimental systems. Here we present initial results using simulations of a room temperature s-band system.
DOI •	reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-TUPDP115
About •	Received ※ 06 October 2023 — Accepted ※ 05 December 2023 — Issued ※ 18 December 2023
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Other Keywords

Page

TUPDP115

Machine Learning for Compact Industrial Accelerators

cavity, controls, simulation, network

846

J.P. Edelen, J.A. Einstein-Curtis, M.J. Henderson, M.C. Kilpatrick
RadiaSoft LLC, Boulder, Colorado, USA
J.A. Diaz Cruz, A.L. Edelen
SLAC, Menlo Park, California, USA

Funding: This material is based upon work supported by the DOE Accelerator R&D and Production under Award Number DE-SC0023641.
The industrial and medical accelerator industry is an ever-growing field with advancements in accelerator technology enabling its adoption for new applications. As the complexity of industrial accelerators grows so does the need for more sophisticated control systems to regulate their operation. Moreover, the environment for industrial and medical accelerators is often harsh and noisy as opposed to the more controlled environment of a laboratory-based machine. This environment makes control more challenging. Additionally, instrumentation for industrial accelerators is limited making it difficult at times to identify and diagnose problems when they occur. RadiaSoft has partnered with SLAC to develop new machine learning methods for control and anomaly detection for industrial accelerators. Our approach is to develop our methods using simulation models followed by testing on experimental systems. Here we present initial results using simulations of a room temperature s-band system.

DOI •

reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-TUPDP115

About •

Received ※ 06 October 2023 — Accepted ※ 05 December 2023 — Issued ※ 18 December 2023

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)