Equivariant Energy Flow Networks for Jet Tagging (2012.00964v2)

Abstract: Jet tagging techniques that make use of deep learning show great potential for improving physics analyses at colliders. One such method is the Energy Flow Network (EFN) - a recently introduced neural network architecture that represents jets as permutation-invariant sets of particle momenta while maintaining infrared and collinear safety. We develop a variant of the Energy Flow Network architecture based on the Deep Sets formalism, incorporating permutation-equivariant layers. We derive conditions under which infrared and collinear safety can be maintained, and study the performance of these networks on the canonical example of W-boson tagging. We find that equivariant Energy Flow Networks have similar performance to Particle Flow Networks, which are superior to standard EFNs. However, equivariant Particle Flow Networks suffer from convergence and overfitting issues. Finally, we study how equivariant networks sculpt the jet mass and provide some initial results on decorrelation using planing.