The Possibility of Using Artificial Neural Networks for the Estimation of Mass Composition of High-Energy Primary Cosmic Ray

Abstract

This paper shows that the artificial neural networks (ANN) can be used for determining the type of particles of high-energy primary cosmic ray (i.e. its mass composition) initiating the EAS. The approach implemented here can be used, e.g., in the Auger experiment. We describe the details of the ANN construction and demonstrate that the program is correct and can be further used to solve physical problems. The network was taught and tested based on the data for the maximum of the EAS development (X_max) and primary energy of a particle initiating this EAS (lg(E0)). The identification of particles based on X_max and lg(E0) resulted in around 80% of correct answers for the light mass composition and 99% for the heavy one. We have a correct answer for the mass composition with domination of one type of particles, i.e. light or heavy. Otherwise, additional parameters should be included as ANN input data.