Application of machine learning in cosmic ray particle identification

Liu Ye; Niu He-Ran; Li Bing-Bing; Ma Xin-Hua; Cui Shu-Wang

doi:10.7498/aps.72.20230334

Abstract

Machine learning algorithms can learn the rules and patterns of big data through computers, excavate potential information hidden behind the data, and be widely used to solve classification, regression, clustering, and other problems. Firstly, this paper uses CORSIKA software to simulate the process of cosmic ray cascade shower in the atmosphere, generating information such as the initial energy, zenith angle, azimuth angle of cosmic ray particles. Then, this paper uses the Geant4 toolkit to conduct thermal neutron detector response simulation, generating 4000 particles in each of proton, helium, CNO, MgAlSi and iron. Based on the experimental simulation data of thermal neutron detector, this paper constructs machine learning models for identifying cosmic ray particles by using decision tree (DT), random forest (RF) and BP neural network (BP NN) respectively. For each particle, all the machine learning algorithms are used for model training based on the simulation data. The cross grid search method is used to adjust the hyper parameters of each machine learning algorithm. The AUC value and Q quality factor value of each algorithm are used as evaluation indexes for particle composition identification. The AUC value is a general indicator for evaluating algorithm performance in machine learning and the Q quality factor value is an evaluation index commonly used in the field of high energy physics. The Experimental results show that different machine learning models have great influence on particle prediction accuracy, and the random forest cosmic ray particle identification model has sufficient accuracy and generalization capability. In the test, the decision tree algorithm adjusted by cross grid search method is sensitive to the medium components (CNO and MgAlSi). The AUC values of the algorithm are all above 0.95 and the Q quality factor values are all above 6. The random forest algorithm adjusted by the cross grid search method has the best effect on the identification of cosmic ray particles. The AUC values of the algorithm are all more than 0.92 and the Q quality factor values are all more than 4. The BP neural network algorithm is only sensitive to proton and iron. This study provides a new method and selection for identifying and screening the cosmic ray particles and it also provides a new idea for the following measurement of cosmic ray energy spectrum by thermal neutron detector.

Keywords:

Authors and contacts

1.
School of Management Science and Engineering, Hebei University of Economics and Business, Shijiazhuang 050061, China
2.
College of Physics, Hebei Normal University, Shijiazhuang 050024, China
3.
Key Laboratory of Particle Astrophysics, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
4.
TIANFU Cosmic Ray Research Center, Chengdu 610000, China

Corresponding author: Cui Shu-Wang, cuisw@hebtu.edu.cn

Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 11905043, U2031103), the Department of Education Project Hebei Province, China (Grant No. KCJSZ2022036), and the Hebei University of Economics and Business Graduate Innovation Funding Project, China (Grant No. XYCX202333).

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References

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[13]	王玉东, 王忠海, 周荣, 陈秀莲, 覃雪, 刘军 2019 核电子学与探测技术 39 567 Wang Y D, Wang Z H, Zhou R, Chen X L, Qin X, Liu J 2019 Nucl. Electron. Detect. Technol. 39 567
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图 1 宇宙线成分鉴别模型技术路线图

Figure 1. Technical roadmap of the cosmic rays component identification model.

DownLoad: Full-Size Img PowerPoint

图 2 随机森林算法建模流程图

Figure 2. Flow chart of random forest algorithm modeling.

DownLoad: Full-Size Img PowerPoint

图 3 本文BP神经网络结构示意图

Figure 3. Structure diagram of BP neural network in this paper

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图 4 三种宇宙线鉴别模型鉴别氦十折交叉验证核验图

Figure 4. Results of three cosmic rays identification models identifying helium using 10-fold cross validation method.

DownLoad: Full-Size Img PowerPoint

图 5 三种宇宙线粒子鉴别模型鉴别氦核概率分布图

Figure 5. Probability distribution of three cosmic rays identification models identifying helium.

DownLoad: Full-Size Img PowerPoint

表 1 决策树鉴别不同成分最佳超参数

Table 1. Optimal hyperparameters of decision tree identifying different components.

超参数	目标成分
超参数	质子	氦核	碳氮氧	镁铝硅	铁核
criterion	Entropy	Entropy	Entropy	Entropy	Entropy
max_depth	21	29	40	28	19
min_samples_split	2	4	7	2	4
min_weight_fraction_leaf	0	0	0	0	0
min_samples_leaf	1	1	1	1	1

DownLoad: CSV

表 2 随机森林鉴别不同成分最佳超参数

Table 2. Optimal hyperparameters of random forest identifying different components.

超参数	目标成分
超参数	质子	氦核	碳氮氧	镁铝硅	铁核
criterion	Gini	Gini	Entropy	Entropy	Entropy
n_estimators	48	88	30	15	21
max_depth	20	26	30	27	23
min_samples_split	2	2	2	1	2
min_samples_leaf	1	1	1	1	1

DownLoad: CSV

表 3 BP神经网络(鉴别氦核)隐藏层节点核验结果

Table 3. BP neural network (identifying helium) hidden layer nodes verification results.

训练结果	隐藏节点个数
训练结果	5	6	7	8	9	10	11	12	13
迭代次数	20000	20000	20000	25000	27000	20000	20000	20000	20000
算法AUC值	0.5503	0.5045	0.5293	0.5593	0.6329	0.6276	0.6177	0.6142	0.6418
Q品质因子	0.82	0.29	0.58	0.86	1.26	1.25	1.22	1.26	1.34

DownLoad: CSV

表 4 BP神经网络鉴别不同成分最佳超参数组合

Table 4. Optimal hyperparameters of BP neural network identifying different components.

超参数	目标成分
超参数	质子	氦核	碳氮氧	镁铝硅	铁核
隐藏层节点数	13	11	13	13	11
初始学习率	0.01	0.01	0.01	0.01	0.01
迭代次数	20000	25000	20000	20000	20000

DownLoad: CSV

表 5 三种宇宙线粒子鉴别模型鉴别不同成分效率及纯度

Table 5. Efficiency and purity of three cosmic rays identification models identifying different components.

目标成分	效率/%			纯度/%
目标成分	BP神经网络	决策树	随机森林	BP神经网络	决策树	随机森林
质子	64.9	74.8	75.7	74.4	77.6	91.1
氦核	36.0	83.3	79.3	52.8	80.1	95.7
碳氮氧	10.3	93.4	81.5	64.5	94.8	99.4
镁铝硅	16.9	91.8	78.7	69.9	92.1	95.8
铁核	82.8	88.1	91.1	87.5	88.7	93.5

DownLoad: CSV

表 6 三种宇宙线粒子鉴别模型鉴别不同成分AUC值及Q品质因子

Table 6. AUC and Q quality factor values of three cosmic rays identification models identifying different components.

目标成分	AUC			Q 品质因子
目标成分	BP神经网络	决策树	随机森林	BP神经网络	决策树	随机森林
质子	0.7962	0.8555	0.9247	2.71	3.15	5.42
氦核	0.6418	0.8805	0.9537	1.34	3.75	8.38
碳氮氧	0.5444	0.9612	0.9739	0.87	7.55	20.1
镁铝硅	0.5754	0.9504	0.9531	1.25	6.54	8.39
铁核	0.8751	0.8952	0.9380	2.96	2.97	4.40

DownLoad: CSV

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[1]	胡红波, 王祥玉, 刘四明 2018 科学通报 63 2440 Google Scholar Hu H B, Wang X Y, Liu S M 2018 Chin. Sci. Bull. 63 2440 Google Scholar
[2]	胡红波, 郭义庆 2016 科学通报 61 1188 Google Scholar Hu H B, Guo Y Q 2016 Chin. Sci. Bull. 61 1188 Google Scholar
[3]	曹臻 2022 科学通报 67 1558 Google Scholar Cao Z 2022 Chin. Sci. Bull. 67 1558 Google Scholar
[4]	曹臻, 陈明君, 陈松战, 胡红波, 刘成, 刘烨, 马玲玲, 马欣华, 盛祥东, 吴含荣, 肖刚, 姚志国, 尹丽巧, 查敏, 张寿山 2019 天文学报 60 3 Google Scholar Cao Z, Chen M J, Chen S Z, Hu H B, Liu C, Liu Y, Ma L L, Ma X H, Shen X D, Wu H R, Xiao G, Yao Z G, Yin L Q, Zha M, Zhang S S 2019 Acta Astron. Sin. 60 3 Google Scholar
[5]	李川, 王文博, 陈鹏飞 2022 中国科学: 物理学力学天文学 52 16 Google Scholar Li C, Wang W B, Chen P F 2022 Sci. China Phys. Mech. 52 16 Google Scholar
[6]	张丰, 刘虎, 祝凤荣 2022 71 249601 Google Scholar Zhang F, Liu H, Zhu F R 2022 Acta Phys. Sin. 71 249601 Google Scholar
[7]	陈秀林 2020 硕士学位论文 (北京: 中国科学院大学) Chen X L 2020 M. S. Thesis (Beijing University of Chinese Academy of Sciences) (in Chinese)
[8]	严雨婷, 毕文杰 2023 中国管理科学网络首发 [2023-02-03] Yan Y T, Bi W J 2023 Chin. J. Manag. Sci. Online First (in Chinese)
[9]	Herrera L J, Todero Peixoto C J, Baños O, Carceller J M, Carrillo F, Guillén A 2020 Entropy 22 998 Google Scholar
[10]	Pang L G, Zhou K, Su N, Petersen H, Stöcker H, Wang X N 2018 Nat. Commun. 9 210 Google Scholar
[11]	高泽鹏, 王永佳, 李庆峰, 刘玲 2022 中国科学: 物理学力学天文学 52 252010 Google Scholar Gao Z P, Wang Y J, Li Q F, Liu L 2022 Sci. China Phys. Mech. 52 252010 Google Scholar
[12]	骆仕杰, 韩抒真 2023 小型微型计算机系统网络首发 [2023-03-05] Luo S J, Han S Z 2023 J. Chin. Comp. Syst. Online First (in Chinese)
[13]	王玉东, 王忠海, 周荣, 陈秀莲, 覃雪, 刘军 2019 核电子学与探测技术 39 567 Wang Y D, Wang Z H, Zhou R, Chen X L, Qin X, Liu J 2019 Nucl. Electron. Detect. Technol. 39 567
[14]	黎威, 龙连春, 刘静毅, 杨洋 2022 71 060202 Google Scholar Li W, Long L C, Liu J Y, Yang Y 2022 Acta Phys. Sin. 71 060202 Google Scholar
[15]	崔静静, 胡泽文, 任萍 2022 情报科学 40 90 Google Scholar Cui J J, Hu Z W, Ren P 2022 Inf. Sci. 40 90 Google Scholar
[16]	李勇男 2018 情报科学 36 80 Google Scholar Li Y N 2018 Inf. Sci. 36 80 Google Scholar
[17]	Lin S, Luo W 2019 Multivar. Behav. Res. 54 578 Google Scholar
[18]	Song S, Park C G 2019 Sustainability 11 6976 Google Scholar
[19]	肖燚, 郭亚会, 李明蔚, 付永硕, 孙峰 2022 北京师范大学学报(自然科学版) 58 261 Google Scholar Xiao Y, Guo Y H, Li M W, Guo Y S, Sun F 2022 J. Beijing Normal Univ. (Nat. Sci.) 58 261 Google Scholar
[20]	卢小宾, 张杨燚, 杨冠灿, 行佳鑫 2022 情报学报 41 1059 Google Scholar Lu X B, Zhang Y Y, Yang G C, Xing J X 2022 Inf. Sci. 41 1059 Google Scholar
[21]	Pei Y L, Li D D, Xue W X 2020 Concurr. Comp-Pract E 32 e5515
[22]	院琳, 杨雪松, 王秉中 2019 68 170503 Google Scholar Yuan L, Yang X S, Wang B Z 2019 Acta Phys. Sin. 68 170503 Google Scholar
[23]	赵振宇, 刘宇帆, 刘善存, 马海波 2023 北京航空航天大学学报 (社会科学版) 网络首发 [2023-03-05] Zhao Z Y, Liu Y F, Liu S C, Ma H B 2023 J. Beijing Univ. Aeronaut. Astronaut (Soc. Sci. Ed.) Online First

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Vol. 72, Issue 14 - 2023-07-20

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