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

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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
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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.

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Distinguishing Primary Cosmic-Ray Composition with Artificial Neural Networks

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