AI in Particle Physics: Applications, Challenges, and Future Prospects

Abstract Particle physics experiments generate large datasets that are too complex to analyze using standard statistical techniques. Machine Learning (ML) and Deep Learning (DL), as a subset of Artificial Intelligence (AI), have turned out to be a highly convenient means of looking at and interpreting this information. In this paper, the application of AI in particle physics is discussed, including such topics as event classification, noise reduction, simulation acceleration, detector optimization, and real-time data analysis. It also discusses the primary applications of experiments such as ATLAS and CMS in CERN and the issues that they are currently experiencing and in the future.