Energy Frontier Exploration using Particle Physics and AI

Artificial Intelligence (AI) and machine learning (ML) methods have proven to be powerful tools for the exploration of physics at the energy frontier of particle physics. Their expanding role in fundamental physics is driven by the challenges of increasingly large and complex data from experiments and computationally expensive simulations required to model and interpret the data, in addition to the rapid development of more powerful AI/ML tools for science-driven data exploration and interpretation. Moreover, exploiting symmetries inherent in physics data have inspired physics-informed ML as a vibrant sub-field of computer science research. I In this talk, I will provide a brief overview of key applications of AI/ML to fundamental physics research at the energy frontier of particle physics and describe several future directions in areas including explainable AI, uncertainty quantification, anomaly detection and real-time AI systems that will significantly enhance the scientific capabilities and opportunities of future experiments. Finally, I will briefly touch on how we are entering a new era in the relationship between AI and science and how scientists will need to learn how to navigate in this new environment.