Application of Stochastic Control Algorithms for the Improvement of the Electron Injection Efficiency of BESSY II
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914796368035840 |
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| author | Schuett, Alexander |
| author_facet | Schuett, Alexander |
| contents | Synchrotron light source storage rings aim to maintain a continuous beam current without observable beam motion during injection. One element that paves the way to this target is the non-linear kicker (NLK). The field distribution it generates poses challenges for optimising the topping-up operation. Within this study, a reinforcement learning agent was developed and trained to optimise the NLK operation parameters. We present the models employed, the optimisation process, and the achieved results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_08824 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Application of Stochastic Control Algorithms for the Improvement of the Electron Injection Efficiency of BESSY II Schuett, Alexander Accelerator Physics Optimization and Control Synchrotron light source storage rings aim to maintain a continuous beam current without observable beam motion during injection. One element that paves the way to this target is the non-linear kicker (NLK). The field distribution it generates poses challenges for optimising the topping-up operation. Within this study, a reinforcement learning agent was developed and trained to optimise the NLK operation parameters. We present the models employed, the optimisation process, and the achieved results. |
| title | Application of Stochastic Control Algorithms for the Improvement of the Electron Injection Efficiency of BESSY II |
| topic | Accelerator Physics Optimization and Control |
| url | https://arxiv.org/abs/2405.08824 |