Explainable Equivariant Neural Networks for Particle Physics: PELICAN

arXiv:2307.16506v4 Announce Type: replace-cross
Abstract: PELICAN is a novel permutation equivariant and Lorentz invariant or covariant aggregator network designed to overcome common limitations found in architectures applied to particle physics problems. Compared to many approaches that use non-specialized architectures that neglect underlying physics principles and require very large numbers of parameters, PELICAN employs a fundamentally symmetry group-based architecture that demonstrates benefits in terms of reduced complexity, increased interpretability, and raw performance. We present a comprehensive study of the PELICAN algorithm …

abstract architectures arxiv covariant cs.lg found hep-ex hep-ph limitations network networks neural networks novel numbers parameters particle particle physics physics type