PELICAN: Permutation Equivariant and Lorentz Invariant or Covariant Aggregator Network for Particle Physics. (arXiv:2211.00454v1 [hep-ph])

Many current approaches to machine learning in particle physics use generic
architectures that require large numbers of parameters and disregard underlying
physics principles, limiting their applicability as scientific modeling tools.
In this work, we present a machine learning architecture that uses a set of
inputs maximally reduced with respect to the full 6-dimensional Lorentz
symmetry, and is fully permutation-equivariant throughout. We study the
application of this network architecture to the standard task of top quark
tagging and show that the …

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