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

Abstract

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.

Authors:
 [1];  [1];  [1]
+ Show Author Affiliations
  1. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Center for Theoretical Physics
Publication Date:
Research Org.:
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP)
OSTI Identifier:
1501473
Grant/Contract Number:  
SC0011090; SC0012567
Resource Type:
Accepted Manuscript
Journal Name:
Journal of High Energy Physics (Online)
Additional Journal Information:
Journal Name: Journal of High Energy Physics (Online); Journal Volume: 2018; Journal Issue: 4; Journal ID: ISSN 1029-8479
Publisher:
Springer Berlin
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; Jets; QCD Phenomenology

Citation Formats

Komiske, Patrick T., Metodiev, Eric M., and Thaler, Jesse. Energy flow polynomials: a complete linear basis for jet substructure. United States: N. p., 2018. Web. doi:10.1007/jhep04(2018)013.
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Komiske, Patrick T., Metodiev, Eric M., & Thaler, Jesse. Energy flow polynomials: a complete linear basis for jet substructure. United States. https://doi.org/10.1007/jhep04(2018)013
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Komiske, Patrick T., Metodiev, Eric M., and Thaler, Jesse. Wed . "Energy flow polynomials: a complete linear basis for jet substructure". United States. https://doi.org/10.1007/jhep04(2018)013. https://www.osti.gov/servlets/purl/1501473.
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@article{osti_1501473,
title = {Energy flow polynomials: a complete linear basis for jet substructure},
author = {Komiske, Patrick T. and Metodiev, Eric M. and Thaler, Jesse},
abstractNote = {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.},
doi = {10.1007/jhep04(2018)013},
journal = {Journal of High Energy Physics (Online)},
number = 4,
volume = 2018,
place = {United States},
year = {2018},
month = {4}
}
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https://doi.org/10.1007/jhep04(2018)013
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Hadron masses and power corrections to event shapes
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Jets and Quantum Field Theory
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Power corrections to event shapes and factorization
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    • Komiske, Patrick T.; Metodiev, Eric M.; Thaler, Jesse
    • Journal of High Energy Physics, Vol. 2019, Issue 1
    • DOI: 10.1007/jhep01(2019)121

    The Lund jet plane
    journal, December 2018

    • Dreyer, Frédéric A.; Salam, Gavin P.; Soyez, Grégory
    • Journal of High Energy Physics, Vol. 2018, Issue 12
    • DOI: 10.1007/jhep12(2018)064

    Learning to classify from impure samples with high-dimensional data
    journal, July 2018

    Automating the construction of jet observables with machine learning
    journal, November 2019

    The Machine Learning landscape of top taggers
    journal, January 2019

    Deep-learning jets with uncertainties and more
    journal, January 2020

    Reports of my demise are greatly exaggerated: $N$-subjettiness taggers take on jet images
    journal, January 2019

    Machine learning and the physical sciences
    journal, December 2019

    Lund jet images from generative and cycle-consistent adversarial networks
    journal, November 2019

    An operational definition of quark and gluon jets
    journal, November 2018

    • Komiske, Patrick T.; Metodiev, Eric M.; Thaler, Jesse
    • Journal of High Energy Physics, Vol. 2018, Issue 11
    • DOI: 10.1007/jhep11(2018)059

    QCD-aware recursive neural networks for jet physics
    journal, January 2019

    • Louppe, Gilles; Cho, Kyunghyun; Becot, Cyril
    • Journal of High Energy Physics, Vol. 2019, Issue 1
    • DOI: 10.1007/jhep01(2019)057

    Interpretable deep learning for two-prong jet classification with jet spectra
    journal, July 2019

    • Chakraborty, Amit; Lim, Sung Hak; Nojiri, Mihoko M.
    • Journal of High Energy Physics, Vol. 2019, Issue 7
    • DOI: 10.1007/jhep07(2019)135

    Automating the construction of jet observables with machine learning
    text, January 2019

    Aspects of track-assisted mass
    journal, March 2019

    • Elder, Benjamin T.; Thaler, Jesse
    • Journal of High Energy Physics, Vol. 2019, Issue 3
    • DOI: 10.1007/jhep03(2019)104

    The Machine Learning landscape of top taggers
    text, January 2019

    Learning to Classify from Impure Samples with High-Dimensional Data
    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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