Beam intensity prediction for ECR ion source using machine learning

Long-term beam stability is one of the important issues in supplying heavy ion beams using an Electron Cyclotron Resonance Ion Source (ECRIS). When the beam intensity decreases in long-term operation, we should tune the ECRIS parameters to restore the original beam intensity. Continuous measurement of the beam intensity using a Faraday cup 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 the beamtime. To resolve this issue, we propose a new method for predicting the beam intensity using machine learning. Our approach uses plasma images, captured through the beam extraction electrode, in addition to the operating parameters as input for the machine learning model. In short-term test datasets, our model has successfully predicted the beam intensity. In addition, ablation studies have shown that plasma images are essential for beam intensity prediction. This presentation will detail the prediction model and its results on the test data.