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Title: Explainable equivariant neural networks for particle physics: PELICAN
A<sc>bstract</sc> 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 theW-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 model complexity and high sample efficiency. On the less common and more complex task of 4-momentum regression, PELICAN also outperforms hand-crafted, non-machine learning algorithms. We discuss the implications of symmetry-restricted architectures for the wider field of machine learning for physics.  more » « less
Award ID(s):
2013010
PAR ID:
10572157
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
Journal of High Energy Physics
Date Published:
Journal Name:
Journal of High Energy Physics
Volume:
2024
Issue:
3
ISSN:
1029-8479
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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