Precise control of high-energy protons transport in space environment by using bayesian optimization
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摘要: 综合考虑地磁场、高能质子的相对论效应以及同步辐射的影响，建立了质子在空间传输的单粒子运动模型。基于该模型，提出利用贝叶斯优化方法，实现给定质子能量下，质子从空间初始点传输到目标点的精确控制方法，获得了出射方向随出射位置的变化规律，当位置径向角取0°和180°时，位置轴向角的取值不会改变粒子的最优出射方向。研究结果为质子束在空间环境中的长程传输提供理论支撑。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.
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Key words:
- high energy proton /
- transport control /
- space transmission /
- Bayesian optimization
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图 1 理论模型与IGRF数据库磁感应强度图
Figure 1. Magnetic induction intensity diagram of theoretical model and IGRF database
图 2 考虑相对论效应和未考虑相对论效应的运动轨迹图
Figure 2. Proton trajectory with and without relativistic effect
图 3 考虑同步辐射和未考虑同步辐射的质子运动轨迹图
Figure 3. Proton trajectory with and without synchrotron radiation
图 5 最小距离与出射速度方向关系图
Figure 5. Relationship between the minimum distance and the exit velocity direction
表 1 不同出射位置的最佳出射方向及最小距离
Table 1. Optimal emission direction and minimum distance at different emission positions
coordinate radial angle/(°) coordinate axial angle/(°) radial angle of exit/(°) axial angle of exit/(°) minimum distance from target/m 0 0 146.34 271.76 2.8 45 0 126.45 214.08 4.8 90 0 14.54 259.39 1.9 135 0 50.89 155.38 1.8 180 0 33.98 92.10 3.2 0 90 146.33 271.77 2.8 45 90 101.28 274.37 3.6 90 90 56.79 86.26 4.7 135 90 11.32 284.69 2.9 180 90 33.99 92.11 3.2 0 180 146.33 271.77 2.8 45 180 128.89 334.47 2.7 90 180 91.02 5.00 3.2 135 180 54.10 34.93 4.5 180 180 33.99 92.11 3.2 0 270 146.33 271.76 2.8 45 270 168.63 104.92 2.4 90 270 79.02 94.64 2.6 135 270 123.92 96.31 3.9 180 270 33.99 92.11 3.2 
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[1] Neubert T, Gilchrist B, Wilderman S, et al. Relativistic electron beam propagation in the Earth's atmosphere: modeling results[J]. Geophysical Research Letters, 1996, 23(9): 1009-1012. doi: 10.1029/96GL00247 [2] Krause L H. The interaction of relativistic electron beams with the near-earth space environment[D]. Ann Arbor: University of Michigan, 1998. [3] Mironychev P V, Babich L P. Propagation of an electron beam in atmosphere at altitudes from 15 to 100 km: numerical experiment[J]. High Temperature, 2000, 38(6): 834-842. doi: 10.1023/A:1004172802986 [4] Porazik P, Johnson J R, Kaganovich I, et al. Modification of the loss cone for energetic particles[J]. Geophysical Research Letters, 2014, 41(22): 8107-8113. doi: 10.1002/2014GL061869 [5] Willard J M, Johnson J R, Snelling J M, et al. Effect of field-line curvature on the ionospheric accessibility of relativistic electron beam experiments[J]. Frontiers in Astronomy and Space Sciences, 2019, 6: 56. doi: 10.3389/fspas.2019.00056 [6] Powis A T, Porazik P, Greklek-Mckeon M, et al. Evolution of a relativistic electron beam for tracing magnetospheric field lines[J]. Frontiers in Astronomy and Space Sciences, 2019, 6: 69. doi: 10.3389/fspas.2019.00069 [7] 郝建红, 王希, 张芳, 等. 随移动窗推进的带电粒子束团长程传输模拟分析[J]. 国防科技大学学报, 2021, 43(5):168-174 doi: 10.11887/j.cn.202105020Hao Jianhong, Wang Xi, Zhang Fang, et al. Simulation analysis of long-range propagation of charged particle beams propelled by moving window[J]. Journal of National University of Defense Technology, 2021, 43(5): 168-174 doi: 10.11887/j.cn.202105020 [8] Hao Jianhong, Wang Xi, Zhang Fang, et al. The influence of magnetic field on the beam quality of relativistic electron beam long-range propagation in near-Earth environment[J]. Plasma Science and Technology, 2021, 23: 115301. doi: 10.1088/2058-6272/ac183a [9] Yao Haibo, Yang Xiaohu, Zhang G B, et al. Stable transport of relativistic electron beams in plasmas[J]. Journal of Plasma Physics, 2022, 88: 905880105. doi: 10.1017/S002237782100132X [10] 钟海坚, 陈宗华, 赵炳炎. 基于MATLAB的地磁场中带电粒子运动模拟分析[J]. 大学物理实验, 2021, 34(1):83-86 doi: 10.14139/j.cnki.cn22-1228.2021.01.022Zhong Haijian, Chen Zonghua, Zhao Bingyan. The motion simulation of charged particles in geomagnetic field based on MATLAB[J]. Physical Experiment of College, 2021, 34(1): 83-86 doi: 10.14139/j.cnki.cn22-1228.2021.01.022 [11] Maus S. IGRF[EB/OL]. [2023-07-15]. https://ccmc.gsfc.nasa.gov/models/IGRF~13/. [12] 李承祖, 银燕, 赵晶, 等. 电动力学[M]. 长沙: 国防科技大学出版社, 2022: 305-307Li Chengzu, Yin Yan, Zhao Jing, et al. Electrodynamics[M]. Changsha: National University of Defense Technology Press, 2022: 305-307 [13] Seeger M. Gaussian processes for machine learning[J]. International Journal of Neural Systems, 2004, 14(2): 69-106. doi: 10.1142/S0129065704001899 [14] Snoek J, Larochelle H, Adams R P. Practical Bayesian optimization of machine learning algorithms[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems. 2012: 2951-2959. [15] Srinivas N, Krause A, Kakade S, et al. Gaussian process optimization in the bandit setting: no regret and experimental design[C]//Proceedings of the 27th International Conference on Machine Learning (ICML). 2010: 1015-1022. [16] Brochu E, Cora V M, de Freitas N. A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning[DB/OL]. arXiv preprint arXiv: 1012.2599, 2010. -
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