Cosmic ray elemental composition study by using an artificial neural network based on the measurement of the lateral particle density distribution in showers induced by primaries in the 30-10000 TeV energy region

摘要

The mass composition plays an important role for understanding the origin of the UHE cosmic rays. Thecomposition in the energy region at and beyond the knee is important because it is related to the sites of cosmic rayproductions and accelerations. In order to perform the composition measurement, an artificial neural network (ANN)has been implemented, it is based on a set of composition estimators obtained by a detailed study of the lateral particledensity distribution. Showers induced by protons, He nuclei, CNO group and iron nuclei have been generated in theenergy region (30-10000) TeV, the lateral particle density distribution was estimated. In this paper the estimators arepresented, the performance of the mass discrimination is discussed.