  Embedding symmetries in the architectures of deep neural networks can improve
classification and network convergence in the context of jet substructure.
These results hint at the existence of symmetries in jet energy depositions,
such as rotational symmetry, arising from physical features of the underlying
processes. We introduce a new family of jet observables, Jet Rotational Metrics
(JRMs), which provide insights into the degree of discrete rotational symmetry
of a jet. We show that JRMs are formidable jet discriminants and that they
capture information not efficiently captured by traditional jet observables,
such as N-subjettiness and Energy-Flow Polynomials.
