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Title: SHAPER: can you hear the shape of a jet?
A bstract The identification of interesting substructures within jets is an important tool for searching for new physics and probing the Standard Model at colliders. Many of these substructure tools have previously been shown to take the form of optimal transport problems, in particular the Energy Mover’s Distance (EMD). In this work, we show that the EMD is in fact the natural structure for comparing collider events, which accounts for its recent success in understanding event and jet substructure. We then present a Shape Hunting Algorithm using Parameterized Energy Reconstruction (S haper ), which is a general framework for defining and computing shape-based observables. S haper generalizes N -jettiness from point clusters to any extended, parametrizable shape. This is accomplished by efficiently minimizing the EMD between events and parameterized manifolds of energy flows representing idealized shapes, implemented using the dual-potential Sinkhorn approximation of the Wasserstein metric. We show how the geometric language of observables as manifolds can be used to define novel observables with built-in infrared-and-collinear safety. We demonstrate the efficacy of the S haper framework by performing empirical jet substructure studies using several examples of new shape-based observables.  more » « less
Award ID(s):
2208392
NSF-PAR ID:
10438952
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Journal of High Energy Physics
Volume:
2023
Issue:
6
ISSN:
1029-8479
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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