|MOPA44||Utilizing Python to Prepare the VENUS Ion Source for Machine Learning||controls, ion-source, interface, ECR||151|
Funding: This work was supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under Contract No. DE-AC02-05CH11231.
The fully superconducting electron cyclotron resonance (ECR) ion source VENUS is one of the world’s two highest-performing ECR ion sources, and a copy of this source will soon be used to produce ion beams at FRIB. The tuning and optimization of ECR ion sources is time consuming, and there are few detailed theoretical models to guide this work. To aid in this process, we are working toward utilizing machine learning to both efficiently optimize VENUS and reliably maintain its stability for long campaigns. We have created a Python library to interface with the programmable logic controller (PLC) in order to operate VENUS and collect and store source and beam data. We will discuss the design and safety considerations that went into creating this library, the implementation of the library, and some of the capabilities it enables.
|Poster MOPA44 [0.862 MB]|
|DOI •||reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA44|
|About •||Received ※ 17 July 2022 — Revised ※ 27 July 2022 — Accepted ※ 05 August 2022 — Issue date ※ 16 August 2022|
|Cite •||reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)|