The IceCube Neutrino Observatory is a large-scale physics experiment constructed at the Amundsen-Scott South Pole Station in Antarctica. It consists of more than five thousand sensors located in the Antarctic ice, distributed over a cubic kilometer. Besides its main purpose of neutrino astronomy, IceCube, and in particular its surface component IceTop, is also used for cosmic ray detection and analysis.
The composition studies of cosmic rays are extremely challenging because of the significant dependence of flux and primary-particle estimation, on the hadronic-interaction model one chooses to interpret the air-shower measurements. This talk will be focused on motivating new techniques which can be used for studying the mass composition and energy spectrum of high-energy cosmic rays measured with IceCube. For the mentioned purpose, I will be using the tools of Neural Networks. This will aim to benefit from the information about the high-energy muons which IceCube provides; in addition to the charged particle component measured at the surface array; hence together acting as a unique three-dimensional cosmic ray detector.