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基于Bagging 算法的神经网络在粒子鉴别中的应用

崔向丽 陈旭荣 喻梅凌 周庆国

崔向丽, 陈旭荣, 喻梅凌, 周庆国. 基于Bagging 算法的神经网络在粒子鉴别中的应用[J]. 原子核物理评论, 2013, 30(4): 446-450. doi: 10.11804/NuclPhysRev.30.04.446
引用本文: 崔向丽, 陈旭荣, 喻梅凌, 周庆国. 基于Bagging 算法的神经网络在粒子鉴别中的应用[J]. 原子核物理评论, 2013, 30(4): 446-450. doi: 10.11804/NuclPhysRev.30.04.446
CUI Xiangli, CHEN Xurong, YU Meiling, ZHOU Qingguo. Application of Bagging Algorithm Based on Neural Network in Particle Identification in Data Analysis[J]. Nuclear Physics Review, 2013, 30(4): 446-450. doi: 10.11804/NuclPhysRev.30.04.446
Citation: CUI Xiangli, CHEN Xurong, YU Meiling, ZHOU Qingguo. Application of Bagging Algorithm Based on Neural Network in Particle Identification in Data Analysis[J]. Nuclear Physics Review, 2013, 30(4): 446-450. doi: 10.11804/NuclPhysRev.30.04.446

基于Bagging 算法的神经网络在粒子鉴别中的应用

doi: 10.11804/NuclPhysRev.30.04.446

Application of Bagging Algorithm Based on Neural Network in Particle Identification in Data Analysis

  • 摘要: 分析了神经网络方法和bagging 算法在实验高能物理和核物理数据分析中的应用现状。分别对神经网络方法和bagging 算法的基本原理进行了介绍。以蒙特卡罗产生器产生的夸克胶子喷注样本为例,详细讨论了神经网络方法以及bagging 算法与神经网络结合对粒子鉴别中信号和背景区分问题的应用过程,并对结果进行了讨论和分析。实验结果表明,应用bagging 算法后,神经网络能够较大幅度地提高实验高能物理和核物理数据分析中粒子鉴别的精度,以及能够得到较高的信噪比。The paper presents the application of neural network and bagging algorithm in experimental high-energy physics and nuclear physics data analysis. Paper also introduces the basic principles of neural network method and bagging algorithm. We use the data samples of quark-gluon jets, which are generated by Monte Carlo generator, to solve the problem of discriminating signal events and background events by the combined algorithm of bagging algorithm and neural network. Experimental results show that, to apply bagging algorithm, neural networks can greatly improve the accuracy of the identification of particles in the experiments of high energy physics and nuclear physical data analysis,and also obtains a larger SNR (Signal to Noise Ratio).
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出版历程
  • 收稿日期:  1900-01-01
  • 修回日期:  1900-01-01
  • 刊出日期:  2013-12-20

基于Bagging 算法的神经网络在粒子鉴别中的应用

doi: 10.11804/NuclPhysRev.30.04.446

摘要: 分析了神经网络方法和bagging 算法在实验高能物理和核物理数据分析中的应用现状。分别对神经网络方法和bagging 算法的基本原理进行了介绍。以蒙特卡罗产生器产生的夸克胶子喷注样本为例,详细讨论了神经网络方法以及bagging 算法与神经网络结合对粒子鉴别中信号和背景区分问题的应用过程,并对结果进行了讨论和分析。实验结果表明,应用bagging 算法后,神经网络能够较大幅度地提高实验高能物理和核物理数据分析中粒子鉴别的精度,以及能够得到较高的信噪比。The paper presents the application of neural network and bagging algorithm in experimental high-energy physics and nuclear physics data analysis. Paper also introduces the basic principles of neural network method and bagging algorithm. We use the data samples of quark-gluon jets, which are generated by Monte Carlo generator, to solve the problem of discriminating signal events and background events by the combined algorithm of bagging algorithm and neural network. Experimental results show that, to apply bagging algorithm, neural networks can greatly improve the accuracy of the identification of particles in the experiments of high energy physics and nuclear physical data analysis,and also obtains a larger SNR (Signal to Noise Ratio).

English Abstract

崔向丽, 陈旭荣, 喻梅凌, 周庆国. 基于Bagging 算法的神经网络在粒子鉴别中的应用[J]. 原子核物理评论, 2013, 30(4): 446-450. doi: 10.11804/NuclPhysRev.30.04.446
引用本文: 崔向丽, 陈旭荣, 喻梅凌, 周庆国. 基于Bagging 算法的神经网络在粒子鉴别中的应用[J]. 原子核物理评论, 2013, 30(4): 446-450. doi: 10.11804/NuclPhysRev.30.04.446
CUI Xiangli, CHEN Xurong, YU Meiling, ZHOU Qingguo. Application of Bagging Algorithm Based on Neural Network in Particle Identification in Data Analysis[J]. Nuclear Physics Review, 2013, 30(4): 446-450. doi: 10.11804/NuclPhysRev.30.04.446
Citation: CUI Xiangli, CHEN Xurong, YU Meiling, ZHOU Qingguo. Application of Bagging Algorithm Based on Neural Network in Particle Identification in Data Analysis[J]. Nuclear Physics Review, 2013, 30(4): 446-450. doi: 10.11804/NuclPhysRev.30.04.446

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