(untitled) (Journal of the College of Basic Education, 2025)
This work uses lateral distribution function (LDF) modeling to tackle the crucial problem of enhancing gamma/proton separation in cosmic ray showers. Even though the classical Nishimura-Kamata-Greisen (NKG) function has been applied extensively to electromagnetic showers, hadronic showers require more sophisticated methods due to their limitations. Three lateral distribution function (LDF) models, the classical NKG, a parametric model based on AIRES simulations, and a recently suggested modified NKG formulation are compared here. Simulation energy of range (1015, 1018 and 1020) and zenith angles (0º, 15º and 25ᵒ), employing both QGSHET-04-II and EPOS-LHC hadronic interaction models. Important results show that the modified NKG is better than the classical NKG, enhancing the gamma/proton separation power. The model’s improved description of secondary particle dispersion, especially in shower periphery, is the source of these improvements. The findings have important ramifications for next-generation observatories like Cherenkov telescope array (CTA), where enhanced LDF parameterization may boost ultra-high-energy gamma detection effective sensitivity. This work lays the groundwork for future enhancements to machine learning-enhanced LDF reconstruction and nucleus-induced showers.