Theoretical Sensitivity Limit of Cosmic Ray Mass Composition with Muon Bundle Energy Loss Parameterization in IceCube Detector.

When a high-energy cosmic ray interacts in the atmosphere, it triggers a cascade of secondary particles, including mesons that decay into muons, neutrinos and other particles. These high energy muons are collectively known as a muon bundle. The number and energy of muons in a bundle are linked to the primary energy and mass of the cosmic ray particle. In our model, we account for both the primary energy and the zenith angle of the incoming cosmic ray. We also consider spatial separation between muons as they move through the ice, which lets us include new parameters to better describe the bundle's energy loss. Based on the parameters obtained for various simulated cosmic ray particles for different primary energies and masses, we employ machine learning techniques to determine how effectively these parameters can distinguish between four cosmic ray primary types: proton, helium, oxygen, and iron. By applying the theoretical model to simulated data, we explore how far we can push the limits of identifying cosmic ray types through the energy loss patterns and spatial separation of muons deep within the ice.