Machine learning methods are becoming essential for high energy physics experiments at facilities like the Large Hadron Collider at CERN. However, existing ML algorithms typically do not account for underlying symmetry properties of the physical systems and interactions of interest, and must learn, often imperfectly, the phenomenological implications of any symmetries involved. This project will develop a new approach based on a novel type of covariant neural network architecture called N-body networks, and will develop a Lorentz invariant neural network software library for systems of particles whose properties are invariant under generalized Lorentz transformations.
Associate Professor, Departments of Computer Science and Statistics
Assistant Professor, Department of Physics, Enrico Fermi Institute, and the College