  Deep Learning approaches are becoming the go-to methods for data analysis in
High Energy Physics (HEP). Nonetheless, most physics-inspired modern
architectures are computationally inefficient and lack interpretability. This
is especially the case with jet tagging algorithms, where computational
efficiency is crucial considering the large amounts of data produced by modern
particle detectors. In this work, we present a novel, versatile and transparent
framework for jet representation; invariant to Lorentz group boosts, which
achieves high accuracy on jet tagging benchmarks while being orders of
magnitudes faster to train and evaluate than other modern approaches for both
supervised and unsupervised schemes.
