Speaker
Description
The KIT accelerator team has contributed significantly to the state-of-the-art of accelerator control especially in controlling electron beams near real-time in storage rings and synchrotrons. Adapted to the latency of components like magnets, slow control optimizes beam transfer and injection into a storage ring [1]. Fast control by online reinforcement learning with AI-on-hardware acceleration reached unprecedented latencies of less than 3 µs without prior training via low-level radiofrequency (RF) manipulation [2]. This enables the control of non-equilibrium processes and dynamics [3]. On a larger scale and on several levels of complexity does the electrical grid also affect the performance of accelerator-based research infrastructures leading to real-time digital twins [4]. The presentation will give an overview of AI deployment for accelerator beam control and accompanying benefits in energy efficiency and sustainability. Our work also prepares for the EC/EU supported project TwinRISE starting in 2027 [5], an initiative of the ARTIFACT network [6].
References
[1] Bayesian optimization of the beam injection process into a storage ring; Xu, C.; Boltz, T.; Mochihashi, A.; Santamaria Garcia, A.; Schuh, M.; Müller, A.-S.; 2023. Phys. Review Accel. Beams 26, 034601. doi: https://doi.org/10.1103/PhysRevAccelBeams.26.034601 ; Top-10 Downloaded Paper in 2023 of Phys. Rev. Accel. Beams.
[2] Microsecond-latency feedback at a particle accelerator by online reinforcement learning on hardware; Scomparin, L.; Caselle, M.; Santamaria Garcia, A.; Xu, C.; Blomley, E.; Dritschler, T.; Mochihashi, A.; Schuh, M.; Steinmann, J. L.; Bründermann, E.; Kopmann, A.; Becker, J.; Müller, A.-S.; Weber, M.; 2026. Machine Learning: Sci. and Technol. 7, 025056. doi: https://doi.org/10.1088/2632-2153/ae5b20
[3] Preliminary results on the reinforcement learning-based control of the microbunching instability; Scomparin, L.; Santamaria Garcia, A.; Kopmann, A.; Mueller, A.-S.; Xu, C.; Blomley, E.; Bründermann, E.; Steinmann, J. L.; Becker, J.; Schuh, M.; Schuh, M.; Caselle, M.; Dritschler, T.; Mochibashi, A.; Weber, M.; 2024. Proceedings of 15th Int. Particle Accel. Conf. 1808–1811. doi: https://doi.org/10.18429/JACoW-IPAC2024-TUPS61
[4] Development of Real-Time Digital Twins for Particle Accelerators, Mohammad Zadeh, M.; Gethmann, J.; Müller, A.-S.; Carne, G. De, 2024. IEEE Energy Conversion Congress and Expo Europe (ECCE 2024), 5p. doi: https://doi.org/10.1109/ECCEEurope62508.2024.10752039
[5] https://cordis.europa.eu/project/id/101287548
[6] https://artifact-network.org/initiatives/twinrise/