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000478846 041__ $$aEnglish
000478846 1001_ $$0P:(DE-H253)PIP1014315$$aStein, Oliver$$b0$$eCorresponding author
000478846 1112_ $$a13th International Particle Accelerator Conference$$cBangkok$$d2022-06-12 - 2022-06-17$$gIPAC'22$$wThailand
000478846 245__ $$aAccelerating Linear Beam Dynamics Simulations for Machine Learning Applications
000478846 260__ $$a[Geneva]$$bJACoW Publishing, Geneva, Switzerland$$c2022
000478846 29510 $$a[Ebook] 13th International Particle Accelerator Conference : June 12-17, 2022, Impact Forum, Muangthong Thani, Bangkok, Thailand : conference proceedings / Chanwattana, Thakonwat , [Geneva] : JACoW Publishing, July 2022,
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000478846 520__ $$aMachine learning has proven to be a powerful tool with many applications in the field of accelerator physics. Training machine learning models is a highly iterative process that requires large numbers of samples. However, beam time is often limited and many of the available simulation frameworks are not optimized for fast computation. As a result, training complex models can be infeasible. In this contribution, we introduce Cheetah, a linear beam dynamics framework optimized for fast computations. We show that Cheetah outperforms existing simulation codes in terms of speed and furthermore demonstrate the application of Cheetah to a reinforcement-learning problem as well as the successful transfer of the Cheetah-trained model to the real world. We anticipate that Cheetah will allow for faster development of more capable machine learning solutions in the field, one day enabling the development of autonomous accelerators.
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000478846 650_7 $$2Other$$aAccelerator Physics
000478846 650_7 $$2Other$$aMC5: Beam Dynamics and EM Fields
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000478846 7001_ $$0P:(DE-H253)PIP1011647$$aAgapov, Ilya$$b1
000478846 7001_ $$0P:(DE-H253)PIP1087213$$aEichler, Annika$$b2
000478846 7001_ $$0P:(DE-H253)PIP1095111$$aKaiser, Jan$$b3
000478846 773__ $$a10.18429/JACoW-IPAC2022-WEPOMS036
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