Speaker
Description
We propose to develop advanced ML models such as neural network (NN) based surrogate models to accurately represent accelerator phase space transport. These surrogate models will enable precise diagnosis and prediction of beam phase space evolution along the beamline, facilitating real-time control and optimization. The developed models will be tested using the Continuous Electron Beam Accelerator Facility (CEBAF) at JLab, providing a pathway toward ML-driven beamline control in operational accelerator environments.
Potential experiments, initially carried out at CEBAF, but later adapted for other facilities, include bunch size and length measurement using existing diagnostics and energy spectrum measurement. We will apply ML models such as Physics-Informed Neural Networks and other neural networks to design an online ML-driven diagnostics platform integrating physics knowledge and diagnostics information. We will also build a virtual beamline, train an reinforcement learning (RL) controller across varied calibration scenarios, and then transfer it to the real machine.
| In which format do you inted to submit your paper? | LaTeX |
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| Preprint marking on your proceeding paper | I do not wish my paper to be marked as preprint. |