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ZT-I-PF-5-6
Autonomous Accelerator (AA)
| Coordinator | Eichler, Annika |
| Grant period | 2020-2022 |
| Funding body | Helmholtz Gemeinschaft Deutscher Forschungszentren |
| HGF | |
| Identifier | G:(DE-HGF)2020_ZT-I-PF-5-6 |
⇧ Impuls- und Vernetzungsfonds ⇧
Note: Modern particle accelerators provide exceptional beams for new discoveries in science. The required flexibility, number of operation modes, and better performance in simultaneously more compact and more energy-efficient accelerators demand advanced control methods. One major challenge is the start-up of such accelerators, which requires frequent manual intervention. Low repetition rates, often only one acceleration event per second, lead to slow optimization rates, thus demanding expert knowledge. Although a complete autonomous accelerator seems far from being reachable, this project takes the first steps by bringing reinforcement learning to accelerator operation. Reinforcement learning yields a policy for every initial state taking the impact of the current action on the future into account, eventually replacing the need for manual intervention. This project focuses on the longitudinal bunch profile of two accelerators located at DESY and KIT. Due to the (sub)-femtosecond requirements on electron bunch duration, nonlinear and collective effects, the control of the longitudinal bunch profile is critical, challenging, and autonomous control will be indispensable for efficient and fast optimization. The high-dimensional space of adjustable parameters is posing a challenge that can only be overcome by deep reinforcement learning algorithms. Further complexity results from the continuous action and state spaces. The choice of a proper reward function is of particular importance and will be tackled here by combining the competences of machine learning experts and accelerator physicists. Results, publications as well as code, will be published open-access.All known publications ...
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Preprint
Bridging the gap between machine learning and particle accelerator physics with high-speed, differentiable simulations
[10.3204/PUBDB-2024-01950]
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Journal Article
Bridging the gap between machine learning and particle accelerator physics with high-speed, differentiable simulations
Physical review accelerators and beams 27(5), 054601 (2024) [10.1103/PhysRevAccelBeams.27.054601]
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Journal Article
Reinforcement learning-trained optimisers and Bayesian optimisation for online particle accelerator tuning
Scientific reports 14(1), 15733 (2024) [10.1038/s41598-024-66263-y]
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Contribution to a conference proceedings/Contribution to a book
Accelerating Linear Beam Dynamics Simulations for Machine Learning Applications
[Ebook] 13th International Particle Accelerator Conference : June 12-17, 2022, Impact Forum, Muangthong Thani, Bangkok, Thailand : conference proceedings / Chanwattana, Thakonwat , [Geneva] : JACoW Publishing, July 2022,
13th International Particle Accelerator Conference, IPAC'22, BangkokBangkok, Thailand, 12 Jun 2022 - 17 Jun 2022
[Geneva] : JACoW Publishing, Geneva, Switzerland 2330-2333 (2022) [10.18429/JACoW-IPAC2022-WEPOMS036]
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All known publications ...
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