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Cheetah: Bridging the Gap Between Machine Learning and Particle Accelerator Physics with High-Speed, Differentiable Simulations

Kaiser, Jan ; Xu, Chenran ORCID iD icon 1; Eichler, Annika; Garcia, Andrea Santamaria 1
1 Institut für Beschleunigerphysik und Technologie (IBPT), Karlsruher Institut für Technologie (KIT)

Abstract:

Machine learning has emerged as a powerful solution to the modern challenges in accelerator physics. However, the limited availability of beam time, the computational cost of simulations, and the high-dimensionality of optimisation problems pose significant challenges in generating the required data for training state-of-the-art machine learning models. In this work, we introduce Cheetah, a PyTorch-based high-speed differentiable linear-beam dynamics code. Cheetah enables the fast collection of large data sets by reducing computation times by multiple orders of magnitude and facilitates efficient gradient-based optimisation for accelerator tuning and system identification. This positions Cheetah as a user-friendly, readily extensible tool that integrates seamlessly with widely adopted machine learning tools. We showcase the utility of Cheetah through five examples, including reinforcement learning training, gradient-based beamline tuning, gradient-based system identification, physics-informed Bayesian optimisation priors, and modular neural network surrogate modelling of space charge effects. The use of such a high-speed differentiable simulation code will simplify the development of machine learning-based methods for particle accelerators and fast-track their integration into everyday operations of accelerator facilities.


Volltext §
DOI: 10.5445/IR/1000168544
Veröffentlicht am 19.02.2024
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Beschleunigerphysik und Technologie (IBPT)
Publikationstyp Forschungsbericht/Preprint
Publikationsjahr 2024
Sprache Englisch
Identifikator KITopen-ID: 1000168544
HGF-Programm 54.11.11 (POF IV, LK 01) Accelerator Operation, Research and Development
Verlag arxiv
Vorab online veröffentlicht am 11.01.2024
Schlagwörter Accelerator Physics (physics.acc-ph), Artificial Intelligence (cs.AI), Machine Learning (cs.LG)
Nachgewiesen in Dimensions
arXiv
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