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RIS citation export for WEB3: Beam intensity prediction using ECR plasma images and machine learning

TY  - CONF
AU  - Morita, Y.
AU  - Kamakura, K.
AU  - Kasagi, A.
AU  - Nishi, T.
AU  - Oka, N.
ED  - Maimone, Fabio
ED  - Schaa, Volker RW
ED  - Mueller, Raphael
TI  - Beam intensity prediction using ECR plasma images and machine learning
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  - Long-term beam stability is one of the important issues in supplying multivalent heavy ion beams using an Electron Cyclotron Resonance Ion Source (ECRIS). When the beam intensity drops for long-term operation, the ECRIS parameters need to be tuned to restore the original beam intensity. Continuous measurement of the beam intensity using a Faraday cup (FC) is impractical while the beam is in use. We have had to rely on an unreliable method of monitoring the total drain current to estimate the beam intensity during beamtime. To resolve this issue, we propose a new method for predicting the beam intensity at FC using machine learning. Our approach incorporates plasma images, captured through a hole in the beam extraction electrode, and operating parameters as input data for the machine learning model. In short-term test datasets, our model has successfully produced rough predictions of the beam intensity. This presentation will detail the prediction model and its prediction results on the test data.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 156
EP  - 158
KW  - plasma
KW  - ECR
KW  - ion-source
KW  - extraction
KW  - operation
DA  - 2024/09
PY  - 2024
SN  - 2222-5692
SN  - 978-3-95450-257-8
DO  - doi:10.18429/JACoW-ECRIS2024-WEB3
UR  - https://jacow.org/ecris2024/papers/web3.pdf
ER  -