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Title: Infrared-safe energy weighting does not guarantee small nonperturbative effects
Infrared and collinear (IRC) safety has long been used a proxy for robustness when developing new jet substructure observables. This guiding philosophy has been carried into the deep learning era, where IRC-safe neural networks have been used for many jet studies. For graph-based neural networks, the most straightforward way to achieve IRC safety is to weight particle inputs by their energies. However, energy-weighting by itself does not guarantee that perturbative calculations of machine-learned observables will enjoy small nonperturbative corrections. In this paper, we demonstrate the sensitivity of IRC-safe networks to nonperturbative effects, by training an energy flow network (EFN) to maximize its sensitivity to hadronization. We then show how to construct Lipschitz energy flow networks ( L -EFNs), which are both IRC safe and relatively insensitive to nonperturbative corrections. We demonstrate the performance of L -EFNs on generated samples of quark and gluon jets, and showcase fascinating differences between the learned latent representations of EFNs and L -EFNs. Published by the American Physical Society2024  more » « less
Award ID(s):
2209443 2019786
PAR ID:
10532075
Author(s) / Creator(s):
; ;
Publisher / Repository:
American Physical Society
Date Published:
Journal Name:
Physical Review D
Volume:
110
Issue:
1
ISSN:
2470-0010
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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