Energy flow polynomials: a complete linear basis for jet substructure

Komiske, Patrick T.; Metodiev, Eric M.; Thaler, Jesse

doi:10.1007/jhep04(2018)013

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

Journal Article · Wed Apr 04 00:00:00 EDT 2018 · Journal of High Energy Physics (Online)

DOI:https://doi.org/10.1007/jhep04(2018)013· OSTI ID:1501473

Komiske, Patrick T. ^[1]; Metodiev, Eric M. ^[1]; Thaler, Jesse ^[1]

Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Center for Theoretical Physics

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.

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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

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Cited by: 84 works

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Correlation functions of local composite operators from generalized unitarity Engelund, O. Tang; Roiban, R. arXiv https://doi.org/10.48550/arxiv.1209.0227	preprint	January 2012
Power Corrections to Event Shapes with Mass-Dependent Operators Mateu, Vicent; Stewart, Iain W.; Thaler, Jesse arXiv https://doi.org/10.48550/arxiv.1209.3781	text	January 2012
Energy Correlation Functions for Jet Substructure Larkoski, Andrew J.; Salam, Gavin P.; Thaler, Jesse arXiv https://doi.org/10.48550/arxiv.1305.0007	text	January 2013
Unsafe but Calculable: Ratios of Angularities in Perturbative QCD Larkoski, Andrew J.; Thaler, Jesse arXiv https://doi.org/10.48550/arxiv.1307.1699	text	January 2013
Searching for Exotic Particles in High-Energy Physics with Deep Learning Baldi, Pierre; Sadowski, Peter; Whiteson, Daniel arXiv https://doi.org/10.48550/arxiv.1402.4735	text	January 2014
Jet-Images: Computer Vision Inspired Techniques for Jet Tagging Cogan, Josh; Kagan, Michael; Strauss, Emanuel arXiv https://doi.org/10.48550/arxiv.1407.5675	text	January 2014
Sudakov Safety in Perturbative QCD Larkoski, Andrew J.; Marzani, Simone; Thaler, Jesse arXiv https://doi.org/10.48550/arxiv.1502.01719	text	January 2015
Jet Substructure Classification in High-Energy Physics with Deep Neural Networks Baldi, Pierre; Bauer, Kevin; Eng, Clara arXiv https://doi.org/10.48550/arxiv.1603.09349	text	January 2016
Improved jet substructure methods: Y-splitter and variants with grooming Dasgupta, Mrinal; Powling, Alexander; Schunk, Lais arXiv https://doi.org/10.48550/arxiv.1609.07149	text	January 2016
Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis de Oliveira, Luke; Paganini, Michela; Nachman, Benjamin arXiv https://doi.org/10.48550/arxiv.1701.05927	text	January 2017
Deep-learning Top Taggers or The End of QCD? Kasieczka, Gregor; Plehn, Tilman; Russell, Michael arXiv https://doi.org/10.48550/arxiv.1701.08784	text	January 2017
Weakly Supervised Classification in High Energy Physics Dery, Lucio Mwinmaarong; Nachman, Benjamin; Rubbo, Francesco arXiv https://doi.org/10.48550/arxiv.1702.00414	text	January 2017
Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters Paganini, Michela; de Oliveira, Luke; Nachman, Benjamin arXiv https://doi.org/10.48550/arxiv.1705.02355	text	January 2017
Operator bases, $S$-matrices, and their partition functions Henning, Brian; Lu, Xiaochuan; Melia, Tom arXiv https://doi.org/10.48550/arxiv.1706.08520	text	January 2017
Factorization and Resummation for Groomed Multi-Prong Jet Shapes Larkoski, Andrew J.; Moult, Ian; Neill, Duff arXiv https://doi.org/10.48550/arxiv.1710.00014	text	January 2017
Exploiting Causal Independence in Bayesian Network Inference Zhang, N. L.; Poole, D. arXiv https://doi.org/10.48550/arxiv.cs/9612101	text	January 1996
Hadron masses and power corrections to event shapes Salam, G. P.; Wicke, D. arXiv https://doi.org/10.48550/arxiv.hep-ph/0102343	text	January 2001
Dispelling the N^3 myth for the Kt jet-finder Cacciari, Matteo; Salam, Gavin P. arXiv https://doi.org/10.48550/arxiv.hep-ph/0512210	text	January 2005
Momentum Flow Correlations from Event Shapes: Factorized Soft Gluons and Soft-Collinear Effective Theory Lee, Christopher; Sterman, George arXiv https://doi.org/10.48550/arxiv.hep-ph/0611061	text	January 2006
Jets and Quantum Field Theory Sveshnikov, N. A.; Tkachov, F. V. arXiv https://doi.org/10.48550/arxiv.hep-ph/9512370	text	January 1995
Measuring Multijet Structure of Hadronic Energy Flow Or What IS A Jet? Tkachov, Fyodor V. arXiv https://doi.org/10.48550/arxiv.hep-ph/9601308	text	January 1996
Power corrections to event shapes and factorization Korchemsky, G. P.; Sterman, G. arXiv https://doi.org/10.48550/arxiv.hep-ph/9902341	text	January 1999
Weakly supervised classification in high energy physics Dery, Lucio Mwinmaarong; Nachman, Benjamin; Rubbo, Francesco Journal of High Energy Physics, Vol. 2017, Issue 5 https://doi.org/10.1007/jhep05(2017)145	journal	May 2017
From correlation functions to event shapes Belitsky, A. V.; Hohenegger, S.; Korchemsky, G. P. arXiv https://doi.org/10.48550/arxiv.1309.0769	text	January 2013

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Spectral analysis of jet substructure with neural networks: boosted Higgs case Lim, Sung Hak; Nojiri, Mihoko M. Journal of High Energy Physics, Vol. 2018, Issue 10 https://doi.org/10.1007/jhep10(2018)181	journal	October 2018
Jets with electrons from boosted top quarks Chatterjee, Suman; Godbole, Rohini; Roy, Tuhin S. Journal of High Energy Physics, Vol. 2020, Issue 1 https://doi.org/10.1007/jhep01(2020)170	journal	January 2020
A theory of quark vs. gluon discrimination Larkoski, Andrew J.; Metodiev, Eric M. Journal of High Energy Physics, Vol. 2019, Issue 10 https://doi.org/10.1007/jhep10(2019)014	journal	October 2019
QCD or what? Heimel, Theo; Kasieczka, Gregor; Plehn, Tilman SciPost Physics, Vol. 6, Issue 3 https://doi.org/10.21468/scipostphys.6.3.030	journal	January 2019
Metric Space of Collider Events Komiske, Patrick T.; Metodiev, Eric M.; Thaler, Jesse Physical Review Letters, Vol. 123, Issue 4 https://doi.org/10.1103/physrevlett.123.041801	journal	July 2019
CapsNets continuing the convolutional quest Diefenbacher, Sascha; Frost, Hermann; Kasieczka, Gregor SciPost Physics, Vol. 8, Issue 2 https://doi.org/10.21468/scipostphys.8.2.023	journal	January 2020
Entropy of a Jet Neill, Duff; Waalewijn, Wouter J. Physical Review Letters, Vol. 123, Issue 14 https://doi.org/10.1103/physrevlett.123.142001	journal	September 2019
Collinear limit of the energy-energy correlator Dixon, Lance J.; Moult, Ian; Zhu, Hua Xing Physical Review D, Vol. 100, Issue 1 https://doi.org/10.1103/physrevd.100.014009	journal	July 2019
Energy flow networks: deep sets for particle jets Komiske, Patrick T.; Metodiev, Eric M.; Thaler, Jesse Journal of High Energy Physics, Vol. 2019, Issue 1 https://doi.org/10.1007/jhep01(2019)121	journal	January 2019
The Lund jet plane Dreyer, Frédéric A.; Salam, Gavin P.; Soyez, Grégory Journal of High Energy Physics, Vol. 2018, Issue 12 https://doi.org/10.1007/jhep12(2018)064	journal	December 2018
Learning to classify from impure samples with high-dimensional data Komiske, Patrick T.; Metodiev, Eric M.; Nachman, Benjamin Physical Review D, Vol. 98, Issue 1 https://doi.org/10.1103/physrevd.98.011502	journal	July 2018
Automating the construction of jet observables with machine learning Datta, Kaustuv; Larkoski, Andrew; Nachman, Benjamin Physical Review D, Vol. 100, Issue 9 https://doi.org/10.1103/physrevd.100.095016	journal	November 2019
The Machine Learning landscape of top taggers Kasieczka, Gregor; Plehn, Tilman; Butter, Anja SciPost Physics, Vol. 7, Issue 1 https://doi.org/10.21468/scipostphys.7.1.014	journal	January 2019
Deep-learning jets with uncertainties and more Bollweg, Sven; Haussmann, Manuel; Kasieczka, Gregor SciPost Physics, Vol. 8, Issue 1 https://doi.org/10.21468/scipostphys.8.1.006	journal	January 2020
Reports of my demise are greatly exaggerated: $N$-subjettiness taggers take on jet images Moore, Liam; Nordström, Karl; Varma, Sreedevi SciPost Physics, Vol. 7, Issue 3 https://doi.org/10.21468/scipostphys.7.3.036	journal	January 2019
Machine learning and the physical sciences Carleo, Giuseppe; Cirac, Ignacio; Cranmer, Kyle Reviews of Modern Physics, Vol. 91, Issue 4 https://doi.org/10.1103/revmodphys.91.045002	journal	December 2019
Lund jet images from generative and cycle-consistent adversarial networks Carrazza, Stefano; Dreyer, Frédéric A. The European Physical Journal C, Vol. 79, Issue 11 https://doi.org/10.1140/epjc/s10052-019-7501-1	journal	November 2019
An operational definition of quark and gluon jets Komiske, Patrick T.; Metodiev, Eric M.; Thaler, Jesse Journal of High Energy Physics, Vol. 2018, Issue 11 https://doi.org/10.1007/jhep11(2018)059	journal	November 2018
QCD-aware recursive neural networks for jet physics Louppe, Gilles; Cho, Kyunghyun; Becot, Cyril Journal of High Energy Physics, Vol. 2019, Issue 1 https://doi.org/10.1007/jhep01(2019)057	journal	January 2019
Interpretable deep learning for two-prong jet classification with jet spectra Chakraborty, Amit; Lim, Sung Hak; Nojiri, Mihoko M. Journal of High Energy Physics, Vol. 2019, Issue 7 https://doi.org/10.1007/jhep07(2019)135	journal	July 2019
Automating the construction of jet observables with machine learning Datta, Kaustuv; Larkoski, Andrew; Nachman, Benjamin ETH Zurich https://doi.org/10.3929/ethz-b-000380014	text	January 2019
Aspects of track-assisted mass Elder, Benjamin T.; Thaler, Jesse Journal of High Energy Physics, Vol. 2019, Issue 3 https://doi.org/10.1007/jhep03(2019)104	journal	March 2019
The Machine Learning landscape of top taggers Kasieczka, Gregor; Plehn, Tilman; Butter, Anja RWTH Aachen University https://doi.org/10.18154/rwth-2019-07568	text	January 2019
Learning to Classify from Impure Samples with High-Dimensional Data Komiske, Patrick T.; Metodiev, Eric M.; Nachman, Benjamin arXiv https://doi.org/10.48550/arxiv.1801.10158	text	January 2018
Spectral Analysis of Jet Substructure with Neural Networks: Boosted Higgs Case Lim, Sung Hak; Nojiri, Mihoko M. arXiv https://doi.org/10.48550/arxiv.1807.03312	text	January 2018
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Energy Flow Networks: Deep Sets for Particle Jets Komiske, Patrick T.; Metodiev, Eric M.; Thaler, Jesse arXiv https://doi.org/10.48550/arxiv.1810.05165	text	January 2018
Automating the Construction of Jet Observables with Machine Learning Datta, Kaustuv; Larkoski, Andrew; Nachman, Benjamin arXiv https://doi.org/10.48550/arxiv.1902.07180	text	January 2019
Interpretable Deep Learning for Two-Prong Jet Classification with Jet Spectra Chakraborty, Amit; Lim, Sung Hak; Nojiri, Mihoko M. arXiv https://doi.org/10.48550/arxiv.1904.02092	text	January 2019
CapsNets Continuing the Convolutional Quest Diefenbacher, Sascha; Frost, Hermann; Kasieczka, Gregor arXiv https://doi.org/10.48550/arxiv.1906.11265	text	January 2019
Lund jet images from generative and cycle-consistent adversarial networks Carrazza, Stefano; Dreyer, Frédéric A. arXiv https://doi.org/10.48550/arxiv.1909.01359	text	January 2019
Report from the A.I. For Nuclear Physics Workshop Bedaque, Paulo; Boehnlein, Amber; Cromaz, Mario arXiv https://doi.org/10.48550/arxiv.2006.05422	preprint	January 2020
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