Application of bagging algorithm based on neural network in particle indentification in data analysis

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). (authors)