Can I read Explainable Equivariant Neural Networks for Particle Physics: PELICAN on EtoBox?

Explainable Equivariant Neural Networks for Particle Physics: PELICAN by Bogatskiy, Alexander; Hoffman, Timothy; Miller, David W.; Offermann, Jan T.; Liu, Xiaoyang is a scholarly article available to read on EtoBox.

What is Explainable Equivariant Neural Networks for Particle Physics: PELICAN about?

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 architecture in the context of both tagging (classification) and reconstructing (regression) Lorentz-boosted top quarks, including the difficult task of specifically identifying and measuring the $W$-boson inside the dense environment of the Lorentz-boosted top-quark hadronic final state. We also extend the application of PELICAN to the tasks of identifying quark-initiated vs.~gluon-initiated jets, and a multi-class identification across five separate target categories of jets. When tested on the standard task of Lorentz-boosted top-quark tagging, PELICAN outperforms existing competitors with much lower mod

Author

Bogatskiy, Alexander; Hoffman, Timothy; Miller, David W.; Offermann, Jan T.; Liu, Xiaoyang

Published

2023

Language

EN