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Particle identification using artificial neural networks at BESⅢ |
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| Authors: | QIN Gang L Jun-Guang HE Kang-Lin BIAN Jian-Ming CAO Guo-Fu DENG Zi-Yan HE Miao HUANG Bin JI Xiao-Bin LI Gang LI Hai-Bo LI Wei-Dong LIU Chun-Xiu LIU Huai-Min MA Qiu-Mei MA Xiang MAO Ya-Jun MAO Ze-Pu MO Xiao-Hu QIU Jin-Fa SUN Sheng-Sen SUN Yong-Zhao WANG Ji-Ke WANG Liang-Liang WEN Shuo-Pin WU Ling-Hui XIE Yu-Guang YOU Zheng-Yun YANG Ming YU Guo-Wei YUAN Chang-Zheng YUAN Ye ZANG Shi-Lei ZHANG Chang-Chun ZHANG Jian-Yong ZHANG Ling ZHANG Xue-Yao ZHANG Yao ZHU Yong-Sheng ZOU Jia-Heng |
| Affiliation: | 1. Institute of High Energy Physics, CAS, Beijing 100049, China;Graduate University of Chinese Academy of Sciences, Beijing 100049, China 2. Institute of High Energy Physics, CAS, Beijing 100049, China 3. Institute of High Energy Physics, CAS, Beijing 100049, China;CCAST(World Laboratory), Beijing 100080, China 4. Peking University, Beijing 100871, China 5. Hunan University, Changsha 410082, China 6. Shandong University, Jinan 250100, China |
| Abstract: | A multilayered perceptrons' neural network technique has been applied in the particle identification at BESⅢ. The networks are trained in each sub-detector level. The NN output of sub-detectors can be sent to a sequential network or be constructed as PDFs for a likelihood. Good muon-ID, electron-ID and hadron-ID are obtained from the networks by using the simulated Monte Carlo samples. |
| Keywords: | artificial neural networks particle identification PID variables multilayered perceptrons artificial neural networks identification simulated Monte Carlo samples Good output sequential likelihood level technique particle perceptrons |
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