Generative Artificial Intelligence for Air Shower Simulation
The detailed simulation of extensive air showers, produced by primary cosmic rays interacting in the atmosphere, is a task that is traditionally undertaken by means of Monte Carlo methods. These processes are computationally intensive, accounting for a major fraction of the computational resources used in the large-scale simulations required by current and future experiments in the field of astroparticle physics. In this work, we present a novel approach based on Generative Adversarial Networks (GANs) to accelerate air shower simulations. We developed and trained a GAN on a dataset of high-energy proton-induced air showers generated with \texttt{CORSIKA}; our model reproduces key distributions of secondary particles, such as energy spectra and spatial distributions at ground level of muons. Once the model has been trained, which takes approximately 74 hours, the generation real time per shower is reduced by a factor of $10^4$ with respect to the full \texttt{CORSIKA} simulation, leading to a substantial decrease in both computational time and energy consumption.