Supervised jet clustering with graph neural networks for Lorentz boosted bosons

Ju, Xiangyang; Nachman, Benjamin

doi:10.1103/PhysRevD.102.075014

Title: Supervised jet clustering with graph neural networks for Lorentz boosted bosons

Abstract

Jet clustering is traditionally an unsupervised learning task because there is no unique way to associate hadronic final states with the quark and gluon degrees of freedom that generated them. However, for uncolored particles like $W$ , $Z$ , and Higgs bosons, it is possible to approximately (though not exactly) associate final state hadrons to their ancestor. By labeling simulated final state hadrons as descending from an uncolored particle, it is possible to train a supervised learning method to create boson jets. Such a method would operate on individual particles and identify connections between particles originating from the same uncolored particle. Graph neural networks are well-suited for this purpose as they can act on unordered sets and naturally create strong connections between particles with the same label. These networks are used to train a supervised jet clustering algorithm. The kinematic properties of these graph jets better match the properties of simulated Lorentz-boosted $W$ bosons. Furthermore, the graph jets contain more information for discriminating $W$ jets from generic quark jets. This work marks the beginning of a new exploration in jet physics to use machine learning to optimize the construction of jets and not only the observables computed from jet constituents.

Authors:

;

Publication Date:: Tue Oct 13 00:00:00 EDT 2020

Research Org.:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)

Sponsoring Org.:: USDOE Office of Science (SC)

OSTI Identifier:: 1781715

Alternate Identifier(s):: OSTI ID: 1764524

Grant/Contract Number:: AC02-05CH11231

Resource Type:: Published Article

Journal Name:: Physical Review D

Additional Journal Information:: Journal Name: Physical Review D Journal Volume: 102 Journal Issue: 7; Journal ID: ISSN 2470-0010

Publisher:: American Physical Society

Country of Publication:: United States

Language:: English

Subject:: 72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; quantum chromodynamics; quark & gluon jets; gauge bosons; hadrons; artificial neural networks; hadron colliders; machine learning

Citation Formats


                    Ju, Xiangyang, and Nachman, Benjamin. Supervised jet clustering with graph neural networks for Lorentz boosted bosons.  United States: N. p., 2020. 
Web.  doi:10.1103/PhysRevD.102.075014.

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                    Ju, Xiangyang, & Nachman, Benjamin. Supervised jet clustering with graph neural networks for Lorentz boosted bosons.  United States.  https://doi.org/10.1103/PhysRevD.102.075014

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                    Ju, Xiangyang, and Nachman, Benjamin. Tue .  
"Supervised jet clustering with graph neural networks for Lorentz boosted bosons".  United States.  https://doi.org/10.1103/PhysRevD.102.075014.

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@article{osti_1781715,

  title        = {Supervised jet clustering with graph neural networks for Lorentz boosted bosons},

  author       = {Ju, Xiangyang and Nachman, Benjamin},

  abstractNote = {Jet clustering is traditionally an unsupervised learning task because there is no unique way to associate hadronic final states with the quark and gluon degrees of freedom that generated them. However, for uncolored particles like W, Z, and Higgs bosons, it is possible to approximately (though not exactly) associate final state hadrons to their ancestor. By labeling simulated final state hadrons as descending from an uncolored particle, it is possible to train a supervised learning method to create boson jets. Such a method would operate on individual particles and identify connections between particles originating from the same uncolored particle. Graph neural networks are well-suited for this purpose as they can act on unordered sets and naturally create strong connections between particles with the same label. These networks are used to train a supervised jet clustering algorithm. The kinematic properties of these graph jets better match the properties of simulated Lorentz-boosted W bosons. Furthermore, the graph jets contain more information for discriminating W jets from generic quark jets. This work marks the beginning of a new exploration in jet physics to use machine learning to optimize the construction of jets and not only the observables computed from jet constituents.},

  doi          = {10.1103/PhysRevD.102.075014},

  journal      = {Physical Review D},

  number       = 7,

  volume       = 102,

  place        = {United States},

  year         = {Tue Oct 13 00:00:00 EDT 2020},

  month        = {Tue Oct 13 00:00:00 EDT 2020}

}

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Title: Supervised jet clustering with graph neural networks for Lorentz boosted bosons

Abstract

Citation Formats

Search for a massive resonance decaying to a pair of Higgs bosons in the four b quark final state in proton–proton collisions at s = 13 TeV journal, June 2018

The anti- k t jet clustering algorithm journal, April 2008

Pulling out all the tops with computer vision and deep learning journal, October 2018

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Erratum to: Search for diboson resonances in hadronic final states in 139 fb−1 of pp collisions at $$ \sqrt{\mathrm{s}} $$ = 13 TeV with the ATLAS detector journal, June 2020

Deep-learned Top Tagging with a Lorentz Layer journal, January 2018

Electromagnetic showers beyond shower shapes journal, January 2020

Combination of CMS searches for heavy resonances decaying to pairs of bosons or leptons journal, November 2019

How much information is in a jet? journal, June 2017

End-to-end jet classification of quarks and gluons with the CMS Open Data journal, October 2020

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End-to-End Physics Event Classification with CMS Open Data: Applying Image-Based Deep Learning to Detector Data for the Direct Classification of Collision Events at the LHC journal, March 2020

JEDI-net: a jet identification algorithm based on interaction networks journal, January 2020

PYTHIA 6.4 physics and manual journal, May 2006

Boosted objects: a probe of beyond the standard model physics journal, June 2011

Search for resonances decaying to a pair of Higgs bosons in the bb¯$$ \overline{\mathrm{b}} $$qq¯$$ \overline{\mathrm{q}} $$’ℓν final state in proton-proton collisions at s$$ \sqrt{s} $$ = 13 TeV journal, October 2019

Deep learning in color: towards automated quark/gluon jet discrimination journal, January 2017

Deep-learning top taggers or the end of QCD? journal, May 2017

Boosted W and Z tagging with jet charge and deep learning journal, March 2020

Jet charge and machine learning journal, October 2018

FastJet user manual: (for version 3.0.2) journal, March 2012

Fuzzy jets journal, June 2016

Topology Classification with Deep Learning to Improve Real-Time Event Selection at the LHC journal, August 2019

Energy flow polynomials: a complete linear basis for jet substructure journal, April 2018

Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques journal, June 2020

Inclusive Search for a Highly Boosted Higgs Boson Decaying to a Bottom Quark-Antiquark Pair journal, February 2018

Calorimetry with deep learning: particle simulation and reconstruction for collider physics journal, July 2020

Jet substructure at the Large Hadron Collider journal, December 2019

Systematics of quark/gluon tagging journal, July 2017

Boosted objects and jet substructure at the LHC. Report of BOOST2012, held at IFIC Valencia, 23rd–27th of July 2012 journal, March 2014

Recombination algorithms and jet substructure: Pruning as a tool for heavy particle searches journal, May 2010

Performance of top-quark and $$\varvec{W}$$ W -boson tagging with ATLAS in Run 2 of the LHC journal, April 2019

Identifying boosted objects with N-subjettiness journal, March 2011

Jet flavor classification in high-energy physics with deep neural networks journal, December 2016

Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning journal, November 2019

Recursive Neural Networks in Quark/Gluon Tagging journal, June 2018

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

Search for diboson resonances in hadronic final states in 139 fb−1 of pp collisions at s$$ \sqrt{s} $$ = 13 TeV with the ATLAS detector journal, September 2019

Jet trimming journal, February 2010

A search for resonances decaying into a Higgs boson and a new particle X in the XH → qqbb final state with the ATLAS detector journal, April 2018

Deep Learning and Its Application to LHC Physics journal, October 2018

Towards an understanding of the correlations in jet substructure: Report of BOOST2013, hosted by the University of Arizona, 12th–16th of August 2013 journal, September 2015

Jet Substructure as a New Higgs-Search Channel at the Large Hadron Collider journal, June 2008

Boosting H → b b ¯ $$ H\to b\overline{b} $$ with machine learning journal, October 2018

Looking Inside Jets: An Introduction to Jet Substructure and Boosted-object Phenomenology book, January 2019

Soft drop journal, May 2014

Maximizing boosted top identification by minimizing N-subjettiness journal, February 2012

A multi-dimensional search for new heavy resonances decaying to boosted $$\text{ W }{}{}$$W $$\text{ W }{}{}$$W , $$\text{ W }{}{}$$W $$\text{ Z }{}{}$$Z , or $$\text{ Z }{}{}$$Z $$\text{ Z }{}{}$$Z boson pairs in the dijet final state at 13 $$\text {Te}\text {V}$$Te journal, March 2020

Jet substructure at the Tevatron and LHC: new results, new tools, new benchmarks journal, May 2012

Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber journal, May 2019

A brief introduction to PYTHIA 8.1 journal, June 2008

In situ calibration of large-radius jet energy and mass in 13 TeV proton–proton collisions with the ATLAS detector journal, February 2019

Search for Low Mass Vector Resonances Decaying to Quark-Antiquark Pairs in Proton-Proton Collisions at s = 13 TeV journal, September 2017

Search for low mass vector resonances decaying into quark-antiquark pairs in proton-proton collisions at s = 13 TeV journal, December 2019

Optimal Jet Finder journal, September 2003

The Machine Learning landscape of top taggers journal, January 2019

Pileup Mitigation with Machine Learning (PUMML) journal, December 2017

Towards jetography journal, May 2010

Jet-images — deep learning edition journal, July 2016

Quark-gluon tagging: Machine learning vs detector journal, January 2019

Jet-images: computer vision inspired techniques for jet tagging journal, February 2015

Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV journal, May 2018

Identification of boosted Higgs bosons decaying into b-quark pairs with the ATLAS detector at 13 $$\text {TeV}$$TeV journal, October 2019

Novel jet observables from machine learning journal, March 2018

Unveiling CP property of top-Higgs coupling with graph neural networks at the LHC journal, March 2020

Search for light resonances decaying to boosted quark pairs and produced in association with a photon or a jet in proton–proton collisions at s = 13 TeV with the ATLAS detector journal, January 2019

Playing tag with ANN: boosted top identification with pattern recognition journal, July 2015

CapsNets continuing the convolutional quest journal, January 2020

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

Searching for exotic particles in high-energy physics with deep learning journal, July 2014

Towards an understanding of jet substructure journal, September 2013

Neural network-based top tagger with two-point energy correlations and geometry of soft emissions journal, July 2020

Pileup mitigation at the Large Hadron Collider with graph neural networks journal, July 2019

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

Search for heavy resonances decaying into two Higgs bosons or into a Higgs boson and a W or Z boson in proton-proton collisions at 13 TeV journal, January 2019

A deep neural network to search for new long-lived particles decaying to jets journal, August 2020

Dijet Resonance Search with Weak Supervision Using s = 13 TeV p p Collisions in the ATLAS Detector journal, September 2020

Search for a massive resonance decaying to a pair of Higgs bosons in the four b quark final state in proton–proton collisions at $\sqrt{s} = 13 TeV$
journal, June 2018

The anti- k _t jet clustering algorithm
journal, April 2008

Pulling out all the tops with computer vision and deep learning
journal, October 2018

Learning representations of irregular particle-detector geometry with distance-weighted graph networks
journal, July 2019

Erratum to: Search for diboson resonances in hadronic final states in 139 fb−1 of pp collisions at $$ \sqrt{\mathrm{s}} $$ = 13 TeV with the ATLAS detector
journal, June 2020

Deep-learned Top Tagging with a Lorentz Layer
journal, January 2018

Electromagnetic showers beyond shower shapes
journal, January 2020

Combination of CMS searches for heavy resonances decaying to pairs of bosons or leptons
journal, November 2019

How much information is in a jet?
journal, June 2017

End-to-end jet classification of quarks and gluons with the CMS Open Data
journal, October 2020

Power counting to better jet observables
journal, December 2014

End-to-End Physics Event Classification with CMS Open Data: Applying Image-Based Deep Learning to Detector Data for the Direct Classification of Collision Events at the LHC
journal, March 2020

JEDI-net: a jet identification algorithm based on interaction networks
journal, January 2020

PYTHIA 6.4 physics and manual
journal, May 2006

Boosted objects: a probe of beyond the standard model physics
journal, June 2011

Search for resonances decaying to a pair of Higgs bosons in the bb¯$$ \overline{\mathrm{b}} $$qq¯$$ \overline{\mathrm{q}} $$’ℓν final state in proton-proton collisions at s$$ \sqrt{s} $$ = 13 TeV
journal, October 2019

Deep learning in color: towards automated quark/gluon jet discrimination
journal, January 2017

Deep-learning top taggers or the end of QCD?
journal, May 2017

Boosted $W$ and $Z$ tagging with jet charge and deep learning
journal, March 2020

Jet charge and machine learning
journal, October 2018

FastJet user manual: (for version 3.0.2)
journal, March 2012

Fuzzy jets
journal, June 2016

Topology Classification with Deep Learning to Improve Real-Time Event Selection at the LHC
journal, August 2019

Energy flow polynomials: a complete linear basis for jet substructure
journal, April 2018

Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
journal, June 2020

Inclusive Search for a Highly Boosted Higgs Boson Decaying to a Bottom Quark-Antiquark Pair
journal, February 2018

Calorimetry with deep learning: particle simulation and reconstruction for collider physics
journal, July 2020

Jet substructure at the Large Hadron Collider
journal, December 2019

Systematics of quark/gluon tagging
journal, July 2017

Boosted objects and jet substructure at the LHC. Report of BOOST2012, held at IFIC Valencia, 23rd–27th of July 2012
journal, March 2014

Recombination algorithms and jet substructure: Pruning as a tool for heavy particle searches
journal, May 2010

Performance of top-quark and $$\varvec{W}$$ W -boson tagging with ATLAS in Run 2 of the LHC
journal, April 2019

Identifying boosted objects with N-subjettiness
journal, March 2011

Jet flavor classification in high-energy physics with deep neural networks
journal, December 2016

Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning
journal, November 2019

Recursive Neural Networks in Quark/Gluon Tagging
journal, June 2018

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

Search for diboson resonances in hadronic final states in 139 fb−1 of pp collisions at s$$ \sqrt{s} $$ = 13 TeV with the ATLAS detector
journal, September 2019

Jet trimming
journal, February 2010

A search for resonances decaying into a Higgs boson and a new particle X in the XH → qqbb final state with the ATLAS detector
journal, April 2018

Deep Learning and Its Application to LHC Physics
journal, October 2018

Towards an understanding of the correlations in jet substructure: Report of BOOST2013, hosted by the University of Arizona, 12th–16th of August 2013
journal, September 2015

Jet Substructure as a New Higgs-Search Channel at the Large Hadron Collider
journal, June 2008

Boosting H → b b ¯ $$ H\to b\overline{b} $$ with machine learning
journal, October 2018

Looking Inside Jets: An Introduction to Jet Substructure and Boosted-object Phenomenology
book, January 2019

Soft drop
journal, May 2014

Maximizing boosted top identification by minimizing N-subjettiness
journal, February 2012

A multi-dimensional search for new heavy resonances decaying to boosted $$\text{ W }{}{}$$W $$\text{ W }{}{}$$W , $$\text{ W }{}{}$$W $$\text{ Z }{}{}$$Z , or $$\text{ Z }{}{}$$Z $$\text{ Z }{}{}$$Z boson pairs in the dijet final state at 13 $$\text {Te}\text {V}$$Te
journal, March 2020

Jet substructure at the Tevatron and LHC: new results, new tools, new benchmarks
journal, May 2012

Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber
journal, May 2019

A brief introduction to PYTHIA 8.1
journal, June 2008

In situ calibration of large-radius jet energy and mass in 13 TeV proton–proton collisions with the ATLAS detector
journal, February 2019

Search for Low Mass Vector Resonances Decaying to Quark-Antiquark Pairs in Proton-Proton Collisions at $\sqrt{s} = 13 TeV$
journal, September 2017

Search for low mass vector resonances decaying into quark-antiquark pairs in proton-proton collisions at $\sqrt{s} = 13 TeV$
journal, December 2019

Optimal Jet Finder
journal, September 2003

The Machine Learning landscape of top taggers
journal, January 2019

Pileup Mitigation with Machine Learning (PUMML)
journal, December 2017

Towards jetography
journal, May 2010

Jet-images — deep learning edition
journal, July 2016

Quark-gluon tagging: Machine learning vs detector
journal, January 2019

Jet-images: computer vision inspired techniques for jet tagging
journal, February 2015

Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV
journal, May 2018

Identification of boosted Higgs bosons decaying into b-quark pairs with the ATLAS detector at 13 $$\text {TeV}$$TeV
journal, October 2019

Novel jet observables from machine learning
journal, March 2018

Unveiling CP property of top-Higgs coupling with graph neural networks at the LHC
journal, March 2020

Search for light resonances decaying to boosted quark pairs and produced in association with a photon or a jet in proton–proton collisions at $\sqrt{s} = 13 TeV$ with the ATLAS detector
journal, January 2019

Playing tag with ANN: boosted top identification with pattern recognition
journal, July 2015

CapsNets continuing the convolutional quest
journal, January 2020

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

Searching for exotic particles in high-energy physics with deep learning
journal, July 2014

Towards an understanding of jet substructure
journal, September 2013

Neural network-based top tagger with two-point energy correlations and geometry of soft emissions
journal, July 2020

Pileup mitigation at the Large Hadron Collider with graph neural networks
journal, July 2019

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

Search for heavy resonances decaying into two Higgs bosons or into a Higgs boson and a W or Z boson in proton-proton collisions at 13 TeV
journal, January 2019

A deep neural network to search for new long-lived particles decaying to jets
journal, August 2020

Dijet Resonance Search with Weak Supervision Using $\sqrt{s} = 13 TeV$ $p p$ Collisions in the ATLAS Detector
journal, September 2020

Parton shower uncertainties in jet substructure analyses with deep neural networks
journal, January 2017