Ai Assisted Particle Physics Simulation

Abstract

The fast development of the Artificial Intelligence (AI) has created new areas of particle physics, especially in simulation, data analysis, and optimizing detectors. The Monte Carlo-based particle simulation, though very accurate, is computationally expensive and time consuming. This study discusses the application of AI, in particular, deep learning, reinforcement learning, and generative models, to simulating particle physics. This paper will discuss the benefits of AI-assisted models in enhancing computational efficiency, predictive accuracy and scalability of high-energy physics experiments based on primary data gathered on 100 physics researchers and simulation engineers. The results indicate that AI can decrease the run times of simulations by up to 60 percent and have an almost identical accuracy of traditional approaches. Nonetheless, there are still issues of interpretability, data quality, and generalization. Its conclusion is that domestic simulation of particles with AI is a revolutionary paradigm in the next generation of experimental physics.