Distinguishing Primary Cosmic-Ray Composition with Artificial Neural Networks

Liang Hualou; Xie Wei; Ren Jingru; Wang Taijie; Dai Guiliang

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

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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, 中国科学院高能物理研究所博士学位论文, 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] CHANG KONG-LIANG . DIMENSIONAL METHOD TO SOLVE THE DIFFUSION CONVECTION EQUATION OF SOLAR COSMIC RAYS. Chinese Physics C, 1978, 2(3): 200-210.
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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

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

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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.