  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.
