摘要
尝试在高山乳胶室实验中用神经网络的方法区分超高能区原初宇宙线当中的质子和原子核,对模拟数据的分析结果表明,当族事例观测能量大于500TeV时,对质子和原子核的分辨率均能稳定在80%附近:而当族事例观测能量在100TeV 和500TeV 之间时,对质子和原子核的分辨率均大于70%.
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.
出处
《高能物理与核物理》
EI
CSCD
北大核心
1997年第3期205-210,共6页
High Energy Physics and Nuclear Physics
基金
国家自然科学基金
关键词
神经网络
原初
宇宙线成分
高山乳胶室
neural networks
genetic algorithm
primary cosmic-ray composition
mountain emulsion chamber
