Particle Identification using Artificial Neural Networks at BESIII

doi:10.1088/1674-1137/32/1/001

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《中国物理C》（英文）编辑部

2024年10月30日

Chinese Physics C> 2008, Vol. 32> Issue(1) : 1-8 DOI: 10.1088/1674-1137/32/1/001

Particle Identification using Artificial Neural Networks at BESIII

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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.

[1]	HAO Gang , QIAO Cong-Feng , ZHANG Ai-Lin . One Interpretation for Recent BES Observation. Chinese Physics C, 2007, 31(9): 840-843.
[2]	GUO Yun-Jun , HE Kang-Lin . Muon Identification Using the Efficiencies Ratio Method at BESⅡ. Chinese Physics C, 2007, 31(11): 1050-1055.
[3]	WU Ling-Hui , LIU Jian-Bei , QIN Zhong-Hua , CHEN Chang , ZHUANG Bao-An , CHEN Yuan-Bo , WANG Yi-Fang , JIN Yan , LIU Rong-Guang , MA Xiao-Yan , Ma Yuan-Yuan , TANG Xiao , , , , . Study of the Compensation Voltage on the Boundary Field Layer of the BES III Drift Chamber. Chinese Physics C, 2006, 30(7): 665-669.
[4]	LIU Fang , HE Kang-Lin , SUN Sheng-Sen , YANG Fan , ZHENG Zhi-Peng , LI Jin . Using the Q of TOF to Identify the Particles. Chinese Physics C, 2005, 29(8): 781-786.
[5]	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.
[6]	JI Xiao-Bin , SHEN Xiao-Yan . Performance Study of p,pin BES Ⅱ Detector. Chinese Physics C, 2003, 27(12): 1058-1061.
[7]	WANG Zheng , YUAN Chang-Zheng , QI Na-Ding . Study of BES Ⅱ Muon Counter Performance. Chinese Physics C, 2003, 27(8): 716-721.
[8]	LI Hui-Hong , LIU Feng , LI Jin , YUAN Chang-Zheng , WANG Zhi-Yong . Study of the p and p Identification at BES Ⅱ. Chinese Physics C, 2003, 27(8): 659-664.
[9]	HUANG GuangShun , CHENG BaoSen , YUAN ChangZheng , LI XinHua , QIXiangRong , LIU Jing , CHEN GuangPei , CHEN JiangChuan , HU HaiMing , HU Tao , JU Xin , LI Jin , MAO ZePu , XUE ShengTian , ZHANG LiangSheng , ZHENG ZhiPeng , ZHOU Li , ZHAO ZhengGuo . New Methods to Measure Integrated Luminosity at BES. Chinese Physics C, 2000, 24(5): 373-378.
[10]	BES Collaboration . J/ψ Physics at BES. Chinese Physics C, 1999, 23(2): 97-102.
[11]	BES Collaboration . Measurements of the Branching Ratios for the Decays J/ψ→γπ⁰,γη, γη'→ 3γ at BES. Chinese Physics C, 1997, 21(5): 385-392.
[12]	Zhang Ziping , Chen Hongfang , Ye Shuwei , Zhao Jiawei . Identification of e,μ,π by Neural Network in BES. Chinese Physics C, 1997, 21(4): 297-303.
[13]	Liang Hualou , Xie Wei , Ren Jingru , Wang Taijie , Dai Guiliang . Distinguishing Primary Cosmic-Ray Composition with Artificial Neural Networks. Chinese Physics C, 1997, 21(3): 205-210.
[14]	Rong Gang , Ma Jimao , Mao Huishun , Bai JingZhi , Xie Peipei , Yan Jie , Wang Yunyong , Mao Zepu , Liu Rongguang , Sun Shijun , Li Weiguo , Song Xiaofei , Wu Zhendong . Study of Particle Identification Using dE/dx in the BES Large Cylindrical Drift Chamber. Chinese Physics C, 1996, 20(7): 577-587.
[15]	Gu Jianhui , Yu Zhongqiang . Muon Sample Selection from Cosmic Ray Events for BES. Chinese Physics C, 1996, 20(1): 26-31.
[16]	Ye Shuwei , Chen Hongfang , Wu Jian , Zhang Ziping , Niu Hongquan , Xu Tong , Tong Guoliang , LV Feng , Li Yong , Zeng Jiyang , Wu Yigen , Yi Kai , Chen Xiangyang , Xu Mi . Relative Likelihood and e/π Separation at BES. Chinese Physics C, 1995, 19(12): 1072-1077.
[17]	Chen Guoming , Chen Gang . Identifying Tau Hadronic Decay with Neural Network. Chinese Physics C, 1995, 19(8): 692-702.
[18]	Zhu Qiming , Zhou Huashi , Jiang Chunhua , Ni Huiling . Determining the Luminosity for BES by Spectrum Analysis of Bhabha Events in Small Angle. Chinese Physics C, 1993, 17(12): 1057-1064.
[19]	BAI JINGZHI , BIAN QING , CHEN LEJUN , CHEN SHENNAN , CHEN YAQING , CHEN ZHAOQING , CHI YANKUN , CUI HUACHUAN , CUI XIANGZONG , DENG SHUEN , DENG YIWEI , DING HUILIANG , DONG BAOZHAONG , DONG XUESHENG , DU XIA , DU ZHIZHEN , FANG CHENG , FENG ZHONG , FU ZHENGSHAN , GAO CUISHAN , GAO MEILI , GAO SHUQI , GAO WENXIU , GAO YUANNING , GU SHUDI , GU WEIX , , , , , , , , , , , , , , , , , , , . Beijing Spectrometer. Chinese Physics C, 1992, 16(9): 769-789.
[20]	CHEN Yuan-Bo , DONG Xue-Sheng , HUANG Yin-Zhi , LI Da-Shi , LIN Wei , LIU Yue-Jun , PAN Zhao-Hui , WU Wei-Min , YANG Chang-You , ZHANG Bing-Yun , ZHANG Chang-Chun , ZHAO Wei-Ren , ZHU Guo-Sheng . THE BES ON-LINE DATA ANALYSIS SYSTEM. Chinese Physics C, 1989, 13(8): 680-688.

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Particle Identification using Artificial Neural Networks at BESIII[J]. Chinese Physics C, 2008, 32(1): 1-8. doi: 10.1088/1674-1137/32/1/001

Particle Identification using Artificial Neural Networks at BESIII[J]. Chinese Physics C, 2008, 32(1): 1-8. doi: 10.1088/1674-1137/32/1/001 shu

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Received: 2007-02-14
Revised: 2007-03-07

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沈阳化工大学材料科学与工程学院沈阳 110142

Particle Identification using Artificial Neural Networks at BESIII

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Received Date: 2007-02-14
Accepted Date: 2007-03-07
Available Online: 2008-01-05

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.