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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