A METHOD FOR DETERMINING THE MASS COMPOSITION OF ULTRA-HIGH ENERGY COSMIC RAYS BY PREDICTING THE DEPTH OF FIRST INTERACTION OF INDIVIDUAL EXTENSIVE AIR SHOWERS

Particle accelerators have been used to characterize the properties of particle and subatomic particles. The most advanced particle accelerators built, LHC, can run at 1017 eV. It is not possible with current technology to accelerate particle to the energies that can be detected by cosmic ray observatories. In the past, by the direct measurements of cosmic rays, scientists discovered sub-atomic particles. Being accelerated to energies higher than 1018 eV, cosmic rays carry important information for particle physics. We have developed a method, which is a combination of Artificial Neural Networks and simple algebraic method that uses parameters from the extensive air shower profile to investigate the mean-free path of the cosmic rays. Method has been tested for cases including one and two component composition with success. Due to lack of experimental measurements, the developed method was not applied to observed events. It will be possible to use the method in the future for an enhanced observatory which will measure the parameters needed