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RIS citation export for WEB2: Applying machine learning techniques to the operation of the superconducting ECR ion source VENUS

TY  - CONF
AU  - Todd, D.S.
AU  - Benitez, J.Y.
AU  - Crawford, H.
AU  - Kireeff, A.
AU  - Lai, Y.S.
AU  - Salathe, M.
AU  - Watson, V.
ED  - Maimone, Fabio
ED  - Schaa, Volker RW
ED  - Mueller, Raphael
TI  - Applying machine learning techniques to the operation of the superconducting ECR ion source VENUS
J2  - Proc. of ECRIS2024, Darmstadt, Germany, 15-19 September 2024
CY  - Darmstadt, Germany
T2  - International Workshop on Electron Cyclotron Resonance Ion Sources
T3  - 26
LA  - english
AB  - An operator of the superconducting ECR ion source VENUS tasked with optimizing the current of a specific ion species or finding a stable operating mode is faced with an operation space composed of ten-to-twenty knobs in which to determine the next move. Machine learning techniques are well-suited to multidimensional optimization spaces. Over the last three years we have been working to employ such techniques with the VENUS ion source. We will present how the introduction of computer control has allowed us to automate tasks such as source baking or to utilize optimization tools to maximize beam currents with no human intervention. Our more recent applications of Bayesian optimization and reinforcement learning to beam current maximization and the maintenance of long term source stability will also be presented. Finally, we will discuss control and diagnostic changes that we have employed to exploit the faster data collection and decision making abilities when VENUS is under computer control.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 152
EP  - 155
KW  - operation
KW  - ion-source
KW  - controls
KW  - ECR
KW  - plasma
DA  - 2024/09
PY  - 2024
SN  - 2222-5692
SN  - 978-3-95450-257-8
DO  - doi:10.18429/JACoW-ECRIS2024-WEB2
UR  - https://jacow.org/ecris2024/papers/web2.pdf
ER  -