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Title: Energy flow polynomials: a complete linear basis for jet substructure

Journal Article · · Journal of High Energy Physics (Online)
 [1];  [1];  [1]
  1. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Center for Theoretical Physics
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We introduce the energy flow polynomials: a complete set of jet substructure observables which form a discrete linear basis for all infrared- and collinear-safe observables. Energy flow polynomials are multiparticle energy correlators with specific angular structures that are a direct consequence of infrared and collinear safety. We establish a powerful graph-theoretic representation of the energy flow polynomials which allows us to design efficient algorithms for their computation. Many common jet observables are exact linear combinations of energy flow polynomials, and we demonstrate the linear spanning nature of the energy flow basis by performing regression for several common jet observables. Using linear classification with energy flow polynomials, we achieve excellent performance on three representative jet tagging problems: quark/gluon discrimination, boosted W tagging, and boosted top tagging. Lastly, the energy flow basis provides a systematic framework for complete investigations of jet substructure using linear methods.

Research Organization:
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), High Energy Physics (HEP)
Grant/Contract Number:
SC0011090; SC0012567
OSTI ID:
1501473
Journal Information:
Journal of High Energy Physics (Online), Vol. 2018, Issue 4; ISSN 1029-8479
Publisher:
Springer BerlinCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 94 works
Citation information provided by
Web of Science

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Cited By (16)

Jets with electrons from boosted top quarks journal January 2020
A theory of quark vs. gluon discrimination journal October 2019
Lund jet images from generative and cycle-consistent adversarial networks journal November 2019
Automating the construction of jet observables with machine learning text January 2019
Aspects of track-assisted mass journal March 2019
The Machine Learning landscape of top taggers text January 2019
Learning to Classify from Impure Samples with High-Dimensional Data text January 2018
Spectral Analysis of Jet Substructure with Neural Networks: Boosted Higgs Case text January 2018
Reports of My Demise Are Greatly Exaggerated: $N$-subjettiness Taggers Take On Jet Images text January 2018
Energy Flow Networks: Deep Sets for Particle Jets text January 2018
Automating the Construction of Jet Observables with Machine Learning text January 2019
Interpretable Deep Learning for Two-Prong Jet Classification with Jet Spectra text January 2019
CapsNets Continuing the Convolutional Quest text January 2019
Lund jet images from generative and cycle-consistent adversarial networks text January 2019
Report from the A.I. For Nuclear Physics Workshop preprint January 2020
ABCDisCo: Automating the ABCD Method with Machine Learning text January 2020

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72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS
Jets
QCD Phenomenology