Chinese Physics C > 1997, Vol. 21 > Issue(3): 205-210

Distinguishing Primary Cosmic-Ray Composition with Artificial Neural Networks

Institute of High Energy Physics,The Chinese Academy of Science,Beijing 100039

Abstract
HTML
Reference
Related

PDF

Abstract：
We used artificial neural networks(ANN) to distinguish superhigh energy cosmic-ray proton(p) and nucleus(N) with Monte Carlo family data in mountain emulsion chamber experiment.The result shows that when visible energy of a family is larger than 500TeV,about 80% of p and N can be correctly selected,and more than 70% can be selected in the region between 100 and 500TeV.

References

[1]

L. Lonnblad et al., Phys. Rev. Lett., 65 (1994) 1321.[2] G. Stimpfl-Abele et al., Computer Phys. Cmmm., 64(1991)46.[3] J. R. Ren et al., Phys. Rev., D38 (1988)1404.[4] D Goldk, Genetic Algorithmsin Search, Optimization and Machine Leaning, Masacheusettg: Addison- Wesley Pub. 1989.[5] S. E. Fahlman, Tech Report., CMV-CS -90- 100.[6] Hualou Liang et al., IEEE ICNNSP'95, p56 - 59, 1995.[7] 梁化楼, 博士学位论文, 1996.[8] B. Widrow et al., 1960 IRE WESCON CON. Record, 96 - 104, Aug. 1960.[9] S. E. Fahlman, Proc. of the 1988 Connectionist Model Summer Schools 1988, 38- 51.[10] C. M. G. Lattes et al., Phys. Rep., 65(1980)151.[11] 任敬儒等, 高能物理与核物理, 16 (1992)193.[12] D. E. Rumelhart et al., Parallel Distributed Processing , Vol. 1-2, MIT Press, 1986.

[1]	,,. Particle Identification using Artificial Neural Networks at BESIII. Chinese Physics C, 2008, 32(1): 1-8.doi:10.1088/1674-1137/32/1/001
[2]	LIU Qian,ZHANG Jia-Wen,CHEN Jin,LI Ru-Bai,ZHAO Jian-Bing,HAN Ji-Feng,XIE Yu-Guang,YAO Ning,QI Na-Ding. Cosmic Ray Test Station for BESⅢ RPC. Chinese Physics C, 2006, 30(4): 327-330.
[3]	YANG Hong-Tao,LIU Jun,GENG Qing-Xi,LIU Chang-Qin,ZHANG Pin,GAO Xiao-Yu,ZHA Min,JING Gui-Ru,JING Cai-Liu,ZHU Qing-Qi,Cheung T,Leung K,NG L. The Study of Primary Cosmic Ray Mass Composition Around the. Chinese Physics C, 2006, 30(6): 497-500.
[4]	MA Li-Na,FENG Cun-Feng,ZHANG Yao,ZHANG Xue-Rao. Identification of Primary γ-Rays in ARGO Experiment at Yangbajing Using ANNS. Chinese Physics C, 2005, 29(5): 485-490.
[5]	YUE Qian,LI Yuan-Jing,CHENG Jian-Ping,WANG Yi,LI Jin,LAI Yong-Fang,LI Qing-Hua,TANG Le. Cosmic-Ray Test and Temperature Effects of MRPC. Chinese Physics C, 2004, 28(11): 1180-1183.
[6]	ZHANG Kun-Shi,LIU Lian-Shou. On the Identification of Quark and Gluon Jets Using Artificial Neural Network Method. Chinese Physics C, 2004, 28(11): 1141-1145.
[7]	LIU Jun,GENG Qing-Xi,YANG Hong-Tao,LIU Chang-Qin,ZHANG Pin,GAO Xiao-Yu,JING Cai-Liu,ZHA Min,JING Gui-Ru,ZHU Qing-Qi,Cheung T,Ng L. Study of EAS Local Muon Density Spectrum and the Knee in the Energy Spectrum of Primary Cosmic Rays. Chinese Physics C, 2003, 27(2): 109-112.
[8]	FENG Gun-Feng,ZHANG Xue-Yao,WANG Yong-Gang,XUE Liang,MA Li-Na,LI Jin-Yu,ZHANG Nai-Jian. Identification of Primary Y-Rays by the Characteristics of Time-Space Structure of the Cosmic-Ray Air Showers. Chinese Physics C, 2003, 27(11): 969-972.
[9]	WANG Yong-Gang,XUE Liang,FENG Cun-Feng,FU Yu,LI Jie,ZHANG Xue-Yao,LI Jin-Yu,ZHANG Nai-Jian,HE Mao,WANG Cheng-Rui,REN Jing-Ru,LU Sui-Ling. Adjustment of Parameters of Primary Cosmic Ray Energy Spectrum with Recent Direct Measurments. Chinese Physics C, 2002, 26(2): 108-115.
[10]	FENG CunFeng,ZHANG XueYao,KONG FanMin,HE Mao,FU Yu,DAI ZhiQiang,ZHANG NaiJian. Relation between Interactive Models and Identification of Primary Cosmic Ray Components at the “Knee”. Chinese Physics C, 2000, 24(6): 467-472.
[11]	Ding Linkal,Zhu Qingqi,Shen Changquan,Yao Zhiguo,Cheung Tsang,Matti Vallinkoski. An Approach to Study the Cosmic Ray ‘Knee’Composition From Underground Multimuon Events at a Shallower Depth. Chinese Physics C, 1999, 23(6): 584-588.
[12]	Xu Chunxian,Dai Hongyue. A Statistic Model for Galactic Cosmic Rays. Chinese Physics C, 1997, 21(6): 486-493.
[13]	Chen Guoming,Chen Gang. Identifying Tau Hadronic Decay with Neural Network. Chinese Physics C, 1995, 19(8): 692-702.
[14]	CAO Pei-Yuan,WANG Cheng-Rui,HE Mao,ZHANG Nai-Jian,LI Jin-Yu,LI Jie,REN Jing-Ru,LU Sui-Ling. High Energy Gamma Rays and Hadrons in Atmospheric Cosmic Rays. Chinese Physics C, 1992, 16(10): 886-890.
[15]	ZHANG Li,ZHANG Ming,MU Jun,YU Chuan-Zan. Propagation of Cosmic-Ray Nuclei with Reacceleration. Chinese Physics C, 1992, 16(5): 392-397.
[16]	DING Lin-Kai. ON THE INELASTICITY OF SUPERHIGH ENERGY COSMIC RAY INTERACTIONS. Chinese Physics C, 1990, 14(6): 486-489.
[17]	JIANG Yin-Lin,YUAN Yu-Kui,LI Yan-Guo,CHEN Duan-Bao,ZHENG Rong-Ting,SONG Jian-Ning. RESEARCH FOR THE POSSIBILITY ON DETECTING COSMIC RAY PARTICLES BY ACOUSTIC WAY. Chinese Physics C, 1986, 10(1): 9-14.
[18]	DING Lin-Kai,JING Gui-Ru. NUCLEAR COMPONENTS OF PRIMARYS COSMIC RAYS AND CLUSTER PHENOMENA IN EMULSION CHAMBER EXPERIMENTS. Chinese Physics C, 1983, 7(2): 129-134.
[19]	Kuang Hao-huai,Hu Wen-jun,Zhou Yin-zao,Tan You-HENG,Shen Chang-quan,Jang Yin-lin,Zhu Qing-qi,Qin Ke-yu,Li Yan-guo,Wang Li-an. IDENTIFICATION OF THE HORIZONTAL COSIMC RAY PENETRATING PARTICLES. Chinese Physics C, 1980, 4(4): 413-416.
[20]	CHANG KONG-LIANG. PROPAGATION EFFECTS OF SOLAR COSMIC RAYS. Chinese Physics C, 1978, 2(5): 385-393.

Access

Get Citation

Liang Hualou, Xie Wei, Ren Jingru, Wang Taijie and Dai Guiliang. Distinguishing Primary Cosmic-Ray Composition with Artificial Neural Networks[J]. Chinese Physics C, 1997, 21(3): 205-210.

Liang Hualou, Xie Wei, Ren Jingru, Wang Taijie and Dai Guiliang. Distinguishing Primary Cosmic-Ray Composition with Artificial Neural Networks[J]. Chinese Physics C, 1997, 21(3): 205-210. shu

RIS(for EndNote,Reference Manager,ProCite)

BibTex

Txt

Milestone

Received: 1900-01-01
Revised: 1900-01-01

Article Metric

Article Views(4757)
PDF Downloads(575)
Cited by(0)

Policy on re-use

To reuse of subscription content published by CPC, the users need to request permission from CPC, unless the content was published under an Open Access license which automatically permits that type of reuse.

通讯作者:陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Distinguishing Primary Cosmic-Ray Composition with Artificial Neural Networks

Corresponding author:Liang Hualou,

Institute of High Energy Physics,The Chinese Academy of Science,Beijing 100039

Received Date:1900-01-01
Accepted Date:1900-01-01
Available Online:1997-03-05

Abstract:We used artificial neural networks(ANN) to distinguish superhigh energy cosmic-ray proton(p) and nucleus(N) with Monte Carlo family data in mountain emulsion chamber experiment.The result shows that when visible energy of a family is larger than 500TeV,about 80% of p and N can be correctly selected,and more than 70% can be selected in the region between 100 and 500TeV.