Abstract
We have studied the feasibility of automatic and fast control of the low energy beam transport section in the newly developed radio-frequency quadrupole at KOMAC (Korea Multipurpose Accelerator Complex) by combining a simple neural network model with a beam profile monitor. Extensive beam dynamics simulations on the proton injector with varying beam transport parameters are performed to generate the training set for the machine learning and to obtain optimization model for the injector. These datasets are well-trained and show good ability to estimate the beam parameters at position under consideration. These results can be a steppingstone to develop an auto-tuning or feedback control system based on artificial intelligence for a high-intensity accelerator. This paper presents the calculation conditions and the training process in detail, as well as the cross-validation of the trained neural network model by using the results obtained by beam dynamics code.
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References
N. Chauvin et al., Rev. Sci. Instrum. 83, 02B320 (2012)
Thomas Wangler, RF Linear Accelerators 2nd edition (Wiley, New York, 2008)
A.L. Edelen et al., IEEE Trans. Nucl. Sci. 63, 2 (2016)
Hyeok-Jung Kwon et al., in Proceedings of the 27th Linear Accelerator Conference (Geneva, Switzerland, 2014)
D. Uriot and N. Pichoff, in 6th International Particle Accelerator Conference (Richmond, VA, USA, 2015)
Hyeok-Jung Kwon, J. Korean Phys. Soc. 69, 967 (2016)
R. Miyamoto et al., in 10th International Particle Accelerator Conference (Melbourne, Australia, 2019)
D. Winklehner et al., Rev. Sci. Instrum. 85, 02A739 (2014)
Elena Fol et al., in 7th International Beam Instrumentation Conference (Shanghai, China, 2018)
Elena Fol et al., in 39th Free Electron Laser Conference (Hamburg, Germany, 2019)
Acknowledgements
This work has been supported through the KOMAC (Korea Multi-purpose Accelerator Complex) operation fund of the Korea Atomic Energy Research Institute (KAERI) by the MSIT (Ministry of Science and ICT).
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Kim, DH., Lim, S., Chung, KJ. et al. Validation of neural networks model based on beam dynamics simulation for an automated control in high-intensity proton injector. J. Korean Phys. Soc. 78, 1185–1190 (2021). https://doi.org/10.1007/s40042-021-00193-0
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DOI: https://doi.org/10.1007/s40042-021-00193-0