Application of Artificial Neural Network to the Identification of Quark and Gluon Jets

Abstract

The Back-Propagation neural network method is used to identify quark and gluon jets generated by Monte Carlo method. The effects of some factors, such as the architecture of neural network, the input parameters, the training precision and the acceptance cut, on the performance of the neural network are studied in detail. The efficiency and purity of identified quark and gluon jets are calculated for different network architectures. It is found that in order to keep the role of all the input parameters balance, they have to be scaled to the same order of magnitude. Through the study on how the efficiency and purity of the identified quark-and gluon-jets vary with the training precision and acceptance cut, a guidance for how to choose these two parameters is given.

Dynamic fuzzy c-means (dFCM) clustering and its application to calorimetric data reconstruction in high-energy physics

“…It is an essential tool for a good decision making model, and has been used in a wide variety of disciplines from bioinformatics to aeronautical engineering to image processing. Computational intelligence has also found its way to detector physics, especially for high energy physics experiments, and has been used to determine detector performances [3,4,5], as well as extract physical information of interacting particles [6,7,8,9].…”