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

Journal Article · · Journal of High Energy Physics (Online)
 [1];  [2];  [2]
  1. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Center for Theoretical Physics; Massachusetts Inst. of Technology, Cambridge (United States)
  2. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Center for Theoretical Physics
+ Show Author Affiliations

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, Cambridge, MA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
Grant/Contract Number:
SC0011090; SC0012567
OSTI ID:
1501473
Journal Information:
Journal of High Energy Physics (Online), Journal Name: Journal of High Energy Physics (Online) Journal Issue: 4 Vol. 2018; ISSN 1029-8479
Publisher:
Springer BerlinCopyright Statement
Country of Publication:
United States
Language:
English

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Cited By (35)

Reports of my demise are greatly exaggerated: $N$-subjettiness taggers take on jet images journal 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
QCD-aware recursive neural networks for jet physics journal January 2019
Energy flow networks: deep sets for particle jets journal January 2019
Jets with electrons from boosted top quarks journal January 2020
Aspects of track-assisted mass journal March 2019
Interpretable deep learning for two-prong jet classification with jet spectra journal July 2019
Boosting H → b b ¯ $$ H\to b\overline{b} $$ with machine learning journal October 2018
Spectral analysis of jet substructure with neural networks: boosted Higgs case journal October 2018
A theory of quark vs. gluon discrimination journal October 2019
An operational definition of quark and gluon jets journal November 2018
The Lund jet plane journal December 2018
Collinear limit of the energy-energy correlator journal July 2019
Automating the construction of jet observables with machine learning journal November 2019
Learning to classify from impure samples with high-dimensional data journal July 2018
Metric Space of Collider Events journal July 2019
Entropy of a Jet journal September 2019
Machine learning and the physical sciences journal December 2019
Lund jet images from generative and cycle-consistent adversarial networks journal November 2019
The Machine Learning landscape of top taggers text January 2019
QCD or what? journal January 2019
The Machine Learning landscape of top taggers journal January 2019
Reports of my demise are greatly exaggerated: $N$-subjettiness taggers take on jet images journal January 2019
Deep-learning jets with uncertainties and more journal January 2020
CapsNets continuing the convolutional quest journal January 2020
Automating the construction of jet observables with machine learning 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

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Related Subjects

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS
Jets
QCD Phenomenology