Jet charge and machine learning

Fraser, Katherine; Schwartz, Matthew D.

doi:10.1007/JHEP10(2018)093

Title: Jet charge and machine learning

Abstract

Modern machine learning techniques, such as convolutional, recurrent and recursive neural networks, have shown promise for jet substructure at the Large Hadron Collider. For example, they have demonstrated effectiveness at boosted top or W boson identification or for quark/gluon discrimination. We explore these methods for the purpose of classifying jets according to their electric charge. We find that both neural networks that incorporate distance within the jet as an input and boosted decision trees including radial distance information can provide significant improvement in jet charge extraction over current methods. Specifically, convolutional, recurrent, and recursive networks can provide the largest improvement over traditional methods, in part by effectively utilizing distance within the jet or clustering history. Furthermore, the advantages of using a fixed-size input representation (as with the CNN) or a small input representation (as with the RNN) suggest that both convolutional and recurrent networks will be essential to the future of modern machine learning at colliders.

Authors:

^[1]; Schwartz, Matthew D. ^[1]

Harvard Univ., Cambridge, MA (United States)

Publication Date:: Mon Oct 15 00:00:00 EDT 2018

Research Org.:: Harvard Univ., Cambridge, MA (United States)

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

OSTI Identifier:: 1483678

Grant/Contract Number:: SC0013607

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: 10; Journal ID: ISSN 1029-8479

Publisher:: Springer Berlin

Country of Publication:: United States

Language:: English

Subject:: 72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; Jets

Citation Formats


                    Fraser, Katherine, and Schwartz, Matthew D. Jet charge and machine learning.  United States: N. p., 2018. 
Web.  doi:10.1007/JHEP10(2018)093.

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                    Fraser, Katherine, & Schwartz, Matthew D. Jet charge and machine learning.  United States.  https://doi.org/10.1007/JHEP10(2018)093

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                    Fraser, Katherine, and Schwartz, Matthew D. Mon .  
"Jet charge and machine learning".  United States.  https://doi.org/10.1007/JHEP10(2018)093.  https://www.osti.gov/servlets/purl/1483678.

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@article{osti_1483678,

  title        = {Jet charge and machine learning},

  author       = {Fraser, Katherine and Schwartz, Matthew D.},

  abstractNote = {Modern machine learning techniques, such as convolutional, recurrent and recursive neural networks, have shown promise for jet substructure at the Large Hadron Collider. For example, they have demonstrated effectiveness at boosted top or W boson identification or for quark/gluon discrimination. We explore these methods for the purpose of classifying jets according to their electric charge. We find that both neural networks that incorporate distance within the jet as an input and boosted decision trees including radial distance information can provide significant improvement in jet charge extraction over current methods. Specifically, convolutional, recurrent, and recursive networks can provide the largest improvement over traditional methods, in part by effectively utilizing distance within the jet or clustering history. Furthermore, the advantages of using a fixed-size input representation (as with the CNN) or a small input representation (as with the RNN) suggest that both convolutional and recurrent networks will be essential to the future of modern machine learning at colliders.},

  doi          = {10.1007/JHEP10(2018)093},

  journal      = {Journal of High Energy Physics (Online)},

  number       = 10,

  volume       = 2018,

  place        = {United States},

  year         = {Mon Oct 15 00:00:00 EDT 2018},

  month        = {Mon Oct 15 00:00:00 EDT 2018}

}

Copy to clipboard

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Beyond $$M_{t\bar{t}}$$: learning to search for a broad $$t\bar{t}$$ resonance at the LHC
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Similar Records

Title: Jet charge and machine learning

Abstract

Citation Formats

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

Substructure of high- p T jets at the LHC journal, April 2009

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

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

Measurement of jet charge in dijet events from s = 8 TeV p p collisions with the ATLAS detector journal, March 2016

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

Top Tagging: A Method for Identifying Boosted Hadronically Decaying Top Quarks journal, October 2008

W -jet tagging: Optimizing the identification of boosted hadronically-decaying W bosons journal, April 2011

Jet substructure classification in high-energy physics with deep neural networks journal, May 2016

Classification without labels: learning from mixed samples in high energy physics journal, October 2017

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

Towards jetography journal, May 2010

Jet-images — deep learning edition journal, July 2016

Calculating the charge of a jet journal, November 2012

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

How to tell quark jets from gluon jets journal, October 1991

Multivariate discrimination and the Higgs+W/Z search journal, April 2011

A parametrization of the properties of quark jets journal, April 1978

Jet Charge at the LHC journal, May 2013

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

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

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

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

Finding gluon jets with a neural trigger journal, September 1990

Light-quark and gluon jet discrimination in $$pp$$ p p collisions at $$\sqrt{s}=7\mathrm {\ TeV}$$ s = 7 TeV with the ATLAS detector journal, August 2014

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

(Machine) learning to do more with less journal, February 2018

A direct observation of quark-gluon jet differences at LEP journal, August 1991

A brief introduction to PYTHIA 8.1 journal, June 2008

Deep-learning top taggers or the end of QCD text, January 2017

Quark and Gluon Tagging at the LHC text, January 2011

Maximizing Boosted Top Identification by Minimizing N-subjettiness text, January 2011

Jet-Images: Computer Vision Inspired Techniques for Jet Tagging text, January 2014

Deep learning in color: towards automated quark/gluon jet discrimination text, January 2016

Deep-learning Top Taggers or The End of QCD? text, January 2017

Recursive Neural Networks in Quark/Gluon Tagging text, January 2017

Beyond $$M_{t\bar{t}}$$: learning to search for a broad $$t\bar{t}$$ resonance at the LHC journal, February 2020

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

QCD-Aware Recursive Neural Networks for Jet Physics text, January 2017

Infrared Safety of a Neural-Net Top Tagging Algorithm text, January 2018

Energy Flow Networks: Deep Sets for Particle Jets text, January 2018

Quark-Gluon Tagging: Machine Learning vs Detector text, January 2018

Automating the Construction of Jet Observables with Machine Learning text, January 2019

A Theory of Quark vs. Gluon Discrimination text, January 2019

Beyond $M_{t\bar{t}}$: learning to search for a broad $t\bar t$ resonance at the LHC text, January 2019

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

Substructure of high- $p_{T}$ jets at the LHC
journal, April 2009

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

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

Measurement of jet charge in dijet events from $\sqrt{s} = 8 TeV$ $p p$ collisions with the ATLAS detector
journal, March 2016

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

Top Tagging: A Method for Identifying Boosted Hadronically Decaying Top Quarks
journal, October 2008

$W$ -jet tagging: Optimizing the identification of boosted hadronically-decaying $W$ bosons
journal, April 2011

Jet substructure classification in high-energy physics with deep neural networks
journal, May 2016

Classification without labels: learning from mixed samples in high energy physics
journal, October 2017

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

Towards jetography
journal, May 2010

Jet-images — deep learning edition
journal, July 2016

Calculating the charge of a jet
journal, November 2012

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

How to tell quark jets from gluon jets
journal, October 1991

Multivariate discrimination and the Higgs+W/Z search
journal, April 2011

A parametrization of the properties of quark jets
journal, April 1978

Jet Charge at the LHC
journal, May 2013

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

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

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

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

Finding gluon jets with a neural trigger
journal, September 1990

Light-quark and gluon jet discrimination in $$pp$$ p p collisions at $$\sqrt{s}=7\mathrm {\ TeV}$$ s = 7 TeV with the ATLAS detector
journal, August 2014

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

(Machine) learning to do more with less
journal, February 2018

A direct observation of quark-gluon jet differences at LEP
journal, August 1991

A brief introduction to PYTHIA 8.1
journal, June 2008

Deep-learning top taggers or the end of QCD
text, January 2017

Quark and Gluon Tagging at the LHC
text, January 2011

Maximizing Boosted Top Identification by Minimizing N-subjettiness
text, January 2011

Jet-Images: Computer Vision Inspired Techniques for Jet Tagging
text, January 2014

Deep learning in color: towards automated quark/gluon jet discrimination
text, January 2016

Deep-learning Top Taggers or The End of QCD?
text, January 2017

Recursive Neural Networks in Quark/Gluon Tagging
text, January 2017

Beyond $$M_{t\bar{t}}$$: learning to search for a broad $$t\bar{t}$$ resonance at the LHC
journal, February 2020

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

QCD-Aware Recursive Neural Networks for Jet Physics
text, January 2017

Infrared Safety of a Neural-Net Top Tagging Algorithm
text, January 2018

Energy Flow Networks: Deep Sets for Particle Jets
text, January 2018

Quark-Gluon Tagging: Machine Learning vs Detector
text, January 2018

Automating the Construction of Jet Observables with Machine Learning
text, January 2019

A Theory of Quark vs. Gluon Discrimination
text, January 2019

Beyond $M_{t\bar{t}}$: learning to search for a broad $t\bar t$ resonance at the LHC
text, January 2019