3. AI for Physics

The chapter delves into the intricate world of particle physics, highlighting the critical role of the Large Hadron Collider in discovering new particles like the Higgs boson. It emphasizes the challenges in detecting short-lived particles and the importance of indirect detection methods. The OmniFold method is introduced as a groundbreaking approach to unfolding observables, using machine learning to iteratively unfold detector-level quantities without binning. This method mitigates issues related to auxiliary features controlling detector response and aligns with standard methods in high-dimensional spaces. The chapter also discusses the integration of reinforcement learning in plasma control for tokamaks, showcasing advancements in magnetic control through machine learning algorithms. Additionally, it touches on the use of neural networks and genetic algorithms in determining the quark-gluon content of nucleons, providing evidence for intrinsic charm quarks. Throughout, the chapter underscores the transformative potential of AI in high-energy physics, making it a must-read for those interested in the intersection of particle physics and advanced data analysis techniques.