Lattice Gauge Theory

Lattice gauge theory uses a discretized spacetime lattice to study non-Abelian gauge theories, aiming to understand fundamental interactions like the strong force. Current research heavily utilizes machine learning techniques, including neural networks (especially gauge-equivariant convolutional networks and normalizing flows), to improve sampling efficiency and overcome challenges like the sign problem in Monte Carlo simulations. These advancements are crucial for more accurate calculations of physical observables, such as hadron masses, and for exploring complex phenomena in quantum field theory, impacting high-energy physics and related fields.