Precise control of high-energy protons transport in space environment by using bayesian optimization

Shen Shiyu; Yang Xiaohu; Zhang Guobo; Zhao Ziqi; Ma Yanyun

doi:10.11884/HPLPB202335.230231

Volume 35 Issue 10

Oct. 2023

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Shen Shiyu, Yang Xiaohu, Zhang Guobo, et al. Precise control of high-energy protons transport in space environment by using bayesian optimization[J]. High Power Laser and Particle Beams, 2023, 35: 104005. doi: 10.11884/HPLPB202335.230231

Citation:

Shen Shiyu, Yang Xiaohu, Zhang Guobo, et al. Precise control of high-energy protons transport in space environment by using bayesian optimization[J]. High Power Laser and Particle Beams, 2023, 35: 104005. doi: 10.11884/HPLPB202335.230231

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Precise control of high-energy protons transport in space environment by using bayesian optimization

doi: 10.11884/HPLPB202335.230231

1.
Department of Nuclear Science and Technology, National University of Defense Technology, Changsha 410073, China
2.
College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China

Received Date: 2023-07-26
Accepted Date: 2023-09-23
Rev Recd Date: 2023-09-19

Available Online: 2023-09-26

Publish Date: 2023-10-08

Abstract

Abstract

Considering the geomagnetic field, the relativistic effect and bremsstrahlung radiation of high-energy protons, a single particle motion model of proton transport in the space environment is established. Based on this model, the Bayesian optimization method is proposed to realize the precise control of protons transport from the initial position to the target under a given proton energy. The dependence of the proton launch angle on the launch height is obtained, that is, when the coordinate radial angle is 0° and 180°, the value of the coordinate axial angle will not change the optimal emission direction of the particles. The results can provide theoretical references for long-distance transport of proton beams in the space environment.
- high energy proton,
- transport control,
- space transmission,
- Bayesian optimization

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References(16)

References

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