Deep learning in color: towards automated quark/gluon jet discrimination

Komiske, Patrick T.; Metodiev, Eric M.; Schwartz, Matthew D.

doi:10.1007/JHEP01(2017)110

Title: Deep learning in color: towards automated quark/gluon jet discrimination

Abstract

Artificial intelligence offers the potential to automate challenging data-processing tasks in collider physics. Here, to establish its prospects, we explore to what extent deep learning with convolutional neural networks can discriminate quark and gluon jets better than observables designed by physicists. Our approach builds upon the paradigm that a jet can be treated as an image, with intensity given by the local calorimeter deposits. We supplement this construction by adding color to the images, with red, green and blue intensities given by the transverse momentum in charged particles, transverse momentum in neutral particles, and pixel-level charged particle counts. Overall, the deep networks match or outperform traditional jet variables. We also find that, while various simulations produce different quark and gluon jets, the neural networks are surprisingly insensitive to these differences, similar to traditional observables. This suggests that the networks can extract robust physical information from imperfect simulations.

Authors:

Komiske, Patrick T. ^[1]; Metodiev, Eric M. ^[1]; Schwartz, Matthew D. ^[2]

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

Publication Date:: Wed Jan 25 00:00:00 EST 2017

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

Sponsoring Org.:: USDOE

OSTI Identifier:: 1360783

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: 2017; Journal Issue: 1; 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


                    Komiske, Patrick T., Metodiev, Eric M., and Schwartz, Matthew D. Deep learning in color: towards automated quark/gluon jet discrimination.  United States: N. p., 2017. 
Web.  doi:10.1007/JHEP01(2017)110.

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                    Komiske, Patrick T., Metodiev, Eric M., & Schwartz, Matthew D. Deep learning in color: towards automated quark/gluon jet discrimination.  United States.  https://doi.org/10.1007/JHEP01(2017)110

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                    Komiske, Patrick T., Metodiev, Eric M., and Schwartz, Matthew D. Wed .  
"Deep learning in color: towards automated quark/gluon jet discrimination".  United States.  https://doi.org/10.1007/JHEP01(2017)110.  https://www.osti.gov/servlets/purl/1360783.

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@article{osti_1360783,

  title        = {Deep learning in color: towards automated quark/gluon jet discrimination},

  author       = {Komiske, Patrick T. and Metodiev, Eric M. and Schwartz, Matthew D.},

  abstractNote = {Artificial intelligence offers the potential to automate challenging data-processing tasks in collider physics. Here, to establish its prospects, we explore to what extent deep learning with convolutional neural networks can discriminate quark and gluon jets better than observables designed by physicists. Our approach builds upon the paradigm that a jet can be treated as an image, with intensity given by the local calorimeter deposits. We supplement this construction by adding color to the images, with red, green and blue intensities given by the transverse momentum in charged particles, transverse momentum in neutral particles, and pixel-level charged particle counts. Overall, the deep networks match or outperform traditional jet variables. We also find that, while various simulations produce different quark and gluon jets, the neural networks are surprisingly insensitive to these differences, similar to traditional observables. This suggests that the networks can extract robust physical information from imperfect simulations.},

  doi          = {10.1007/JHEP01(2017)110},

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

  number       = 1,

  volume       = 2017,

  place        = {United States},

  year         = {Wed Jan 25 00:00:00 EST 2017},

  month        = {Wed Jan 25 00:00:00 EST 2017}

}

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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 improvementmore »« less
Cited by 44
https://doi.org/10.1007/JHEP10(2018)093

Full Text Available
Predicting wind-driven spatial deposition through simulated color images using deep autoencoders

Journal Article Fernández-Godino, M. Giselle ; Lucas, Donald D. ; Kong, Qingkai - Scientific Reports

Abstract For centuries, scientists have observed nature to understand the laws that govern the physical world. The traditional process of turning observations into physical understanding is slow. Imperfect models are constructed and tested to explain relationships in data. Powerful new algorithms can enable computers to learn physics by observing images and videos. Inspired by this idea, instead of training machine learning models using physical quantities, we used images, that is, pixel information. For this work, and as a proof of concept, the physics of interest are wind-driven spatial patterns. These phenomena include features in Aeolian dunes and volcanic ash deposition,more »« less
https://doi.org/10.1038/s41598-023-28590-4
A theory of quark vs. gluon discrimination

Journal Article Larkoski, Andrew J. ; Metodiev, Eric M. - Journal of High Energy Physics (Online)

Understanding jets initiated by quarks and gluons is of fundamental importance in collider physics. Efficient and robust techniques for quark versus gluon jet discrimination have consequences for new physics searches, precision αs studies, parton distribution function extractions, and many other applications. Numerous machine learning analyses have attacked the problem, demonstrating that good performance can be obtained but generally not providing an understanding for what properties of the jets are responsible for that separation power. In this paper, we provide an extensive and detailed analysis of quark versus gluon discrimination from first-principles theoretical calculations. Working in the strongly-ordered soft and collinear limits, we calculate probability distributions for fixed N-body kinematics within jets with up through three resolved emissions (O(αmore »« less
https://doi.org/10.1007/jhep10(2019)014

Full Text Available
Measurement of the t$$\bar{t}$$ production cross section in the E_T+jets channel at CDF

Thesis/Dissertation Compostella, Gabriele

This thesis is focused on an inclusive search of the tmore »« less
https://doi.org/10.2172/935790

Full Text Available
Quark and gluon jets at CDF

Conference Wainer, N ; CDF Collaboration

Jet shapes at {radical}s = 1.8 TeV have been measured by CDF at the Fermilab Tevatron Collider. The jet shape is mainly formed by parton emission before hadronization. The jet shapes may also be used to differ between quark and gluon jets. Two approaches are tried. Jet shape variables are input to a feed forward neural network trained on QCD Monte Carlos. We observe a variation of the quark content of jets as a function of jet transverse energy. The second approach consists of constructing a likelihood function, based on Monte Carlo predictions, with the Mellin moments of the momentummore »« less
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Title: Deep learning in color: towards automated quark/gluon jet discrimination

Abstract

Citation Formats

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

Rotation-invariant convolutional neural networks for galaxy morphology prediction journal, April 2015

Associated jet and subjet rates in light-quark and gluon jet discrimination journal, April 2015

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

Multilayer feedforward networks are universal approximators journal, January 1989

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

Energy correlation functions for jet substructure journal, June 2013

Enhanced Higgs Boson to τ + τ − Search with Deep Learning journal, March 2015

A determination of parton distributions with faithful uncertainty estimation journal, March 2009

Jet-images — deep learning edition journal, July 2016

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

Factorization for groomed jet substructure beyond the next-to-leading logarithm journal, July 2016

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

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

A study of differences between quark and gluon jets using vertex tagging of quark jets journal, September 1993

Herwig 7.0/Herwig++ 3.0 release note journal, April 2016

Search for supersymmetry in events with opposite-sign dileptons and missing transverse energy using an artificial neural network journal, April 2013

An introduction to PYTHIA 8.2 journal, June 2015

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

Quark and gluon jet substructure journal, April 2013

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

Performance of Tracking, b-tagging and Jet/MET reconstruction at the CMS High Level Trigger journal, December 2015

Deep learning in neural networks: An overview journal, January 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

Herwig++ physics and manual journal, November 2008

Quark and Gluon Tagging at the LHC journal, October 2011

Gaining (mutual) information about quark/gluon discrimination journal, November 2014

Role of neural networks in the search of the Higgs boson at LHC journal, September 1997

A neural network clustering algorithm for the ATLAS silicon pixel detector journal, September 2014

Performance of b -jet identification in the ATLAS experiment journal, January 2016

Performance of τq-lepton reconstruction and identification in CMS journal, January 2012

Going deeper with convolutions conference, June 2015

Future possibilities for artificial intelligence in the practical management of hypertension journal, July 2020

Search for supersymmetry in events with opposite-sign dileptons and missing transverse energy using an artificial neural network text, January 2013

Herwig 7.0/Herwig++ 3.0 release note text, January 2016

Performance of τ-lepton reconstruction and identification in CMS text, January 2012

A neural network clustering algorithm for the ATLAS silicon pixel detector text, January 2014

The anti-k_t jet clustering algorithm text, January 2008

Herwig++ Physics and Manual text, January 2008

Quark and Gluon Tagging at the LHC text, January 2011

Search for supersymmetry in events with opposite-sign dileptons and missing transverse energy using an artificial neural network text, January 2013

Energy Correlation Functions for Jet Substructure text, January 2013

Searching for Exotic Particles in High-Energy Physics with Deep Learning text, January 2014

Deep Learning in Neural Networks: An Overview text, January 2014

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

Going Deeper with Convolutions preprint, January 2014

Associated jet and subjet rates in light-quark and gluon jet discrimination text, January 2015

Herwig 7.0 / Herwig++ 3.0 Release Note text, January 2015

Factorization for groomed jet substructure beyond the next-to-leading logarithm text, January 2016

Role of Neural Networks in the Search of the Higgs Boson at LHC text, January 1997

Signal-background discrimination with convolutional neural networks in the PandaX-III experiment using MC simulation journal, August 2018

Quark jet versus gluon jet: fully-connected neural networks with high-level features journal, June 2019

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

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

Supervised Deep Learning in High Energy Phenomenology: a Mini Review journal, August 2019

Modelling non-markovian quantum processes with recurrent neural networks journal, December 2018

Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC journal, September 2018

Solving differential equations with neural networks: Applications to the calculation of cosmological phase transitions journal, July 2019

Deep learning for R -parity violating supersymmetry searches at the LHC journal, October 2018

A case study of quark-gluon discrimination at NNLL $$'$$ ′ in comparison to parton showers journal, November 2017

Machine learning uncertainties with adversarial neural networks journal, January 2019

JUNIPR: a framework for unsupervised machine learning in particle physics journal, February 2019

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

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

Deep learning at 15PF: supervised and semi-supervised classification for scientific data conference, January 2017

Using Random Forests on Real-World City Data for Urban Planning in a Visual Semantic Decision Support System journal, May 2019

A case study of quark-gluon discrimination at NNLL' in comparison to parton showers text, January 2017

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

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

Study of energy deposition patterns in hadron calorimeter for prompt and displaced jets using convolutional neural network text, January 2019

A Case Study of Quark-Gluon Discrimination at NNLL$'$ in Comparison to Parton Showers text, January 2017

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

Portraying Double Higgs at the Large Hadron Collider conference, January 2021

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

Weakly Supervised Classification in High Energy Physics text, January 2017

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

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

Rotation-invariant convolutional neural networks for galaxy morphology prediction
journal, April 2015

Associated jet and subjet rates in light-quark and gluon jet discrimination
journal, April 2015

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

Multilayer feedforward networks are universal approximators
journal, January 1989

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

Energy correlation functions for jet substructure
journal, June 2013

Enhanced Higgs Boson to $τ^{+} τ^{-}$ Search with Deep Learning
journal, March 2015

A determination of parton distributions with faithful uncertainty estimation
journal, March 2009

Jet-images — deep learning edition
journal, July 2016

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

Factorization for groomed jet substructure beyond the next-to-leading logarithm
journal, July 2016

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

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

A study of differences between quark and gluon jets using vertex tagging of quark jets
journal, September 1993

Herwig 7.0/Herwig++ 3.0 release note
journal, April 2016

Search for supersymmetry in events with opposite-sign dileptons and missing transverse energy using an artificial neural network
journal, April 2013

An introduction to PYTHIA 8.2
journal, June 2015

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

Quark and gluon jet substructure
journal, April 2013

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

Performance of Tracking, b-tagging and Jet/MET reconstruction at the CMS High Level Trigger
journal, December 2015

Deep learning in neural networks: An overview
journal, January 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

Herwig++ physics and manual
journal, November 2008

Quark and Gluon Tagging at the LHC
journal, October 2011

Gaining (mutual) information about quark/gluon discrimination
journal, November 2014

Role of neural networks in the search of the Higgs boson at LHC
journal, September 1997

A neural network clustering algorithm for the ATLAS silicon pixel detector
journal, September 2014

Performance of b -jet identification in the ATLAS experiment
journal, January 2016

Performance of τq-lepton reconstruction and identification in CMS
journal, January 2012

Going deeper with convolutions
conference, June 2015

Future possibilities for artificial intelligence in the practical management of hypertension
journal, July 2020

Search for supersymmetry in events with opposite-sign dileptons and missing transverse energy using an artificial neural network
text, January 2013

Herwig 7.0/Herwig++ 3.0 release note
text, January 2016

Performance of τ-lepton reconstruction and identification in CMS
text, January 2012

A neural network clustering algorithm for the ATLAS silicon pixel detector
text, January 2014

The anti-k_t jet clustering algorithm
text, January 2008

Herwig++ Physics and Manual
text, January 2008

Quark and Gluon Tagging at the LHC
text, January 2011

Search for supersymmetry in events with opposite-sign dileptons and missing transverse energy using an artificial neural network
text, January 2013

Energy Correlation Functions for Jet Substructure
text, January 2013

Searching for Exotic Particles in High-Energy Physics with Deep Learning
text, January 2014

Deep Learning in Neural Networks: An Overview
text, January 2014

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

Going Deeper with Convolutions
preprint, January 2014

Associated jet and subjet rates in light-quark and gluon jet discrimination
text, January 2015

Herwig 7.0 / Herwig++ 3.0 Release Note
text, January 2015

Factorization for groomed jet substructure beyond the next-to-leading logarithm
text, January 2016

Role of Neural Networks in the Search of the Higgs Boson at LHC
text, January 1997

Signal-background discrimination with convolutional neural networks in the PandaX-III experiment using MC simulation
journal, August 2018

Quark jet versus gluon jet: fully-connected neural networks with high-level features
journal, June 2019

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

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

Supervised Deep Learning in High Energy Phenomenology: a Mini Review
journal, August 2019

Modelling non-markovian quantum processes with recurrent neural networks
journal, December 2018

Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC
journal, September 2018

Solving differential equations with neural networks: Applications to the calculation of cosmological phase transitions
journal, July 2019

Deep learning for $R$ -parity violating supersymmetry searches at the LHC
journal, October 2018

A case study of quark-gluon discrimination at NNLL $$'$$ ′ in comparison to parton showers
journal, November 2017

Machine learning uncertainties with adversarial neural networks
journal, January 2019

JUNIPR: a framework for unsupervised machine learning in particle physics
journal, February 2019

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

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

Deep learning at 15PF: supervised and semi-supervised classification for scientific data
conference, January 2017

Using Random Forests on Real-World City Data for Urban Planning in a Visual Semantic Decision Support System
journal, May 2019

A case study of quark-gluon discrimination at NNLL' in comparison to parton showers
text, January 2017

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

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

Study of energy deposition patterns in hadron calorimeter for prompt and displaced jets using convolutional neural network
text, January 2019

A Case Study of Quark-Gluon Discrimination at NNLL$'$ in Comparison to Parton Showers
text, January 2017

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

Portraying Double Higgs at the Large Hadron Collider
conference, January 2021

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

Weakly Supervised Classification in High Energy Physics
text, January 2017

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

Systematics of quark/gluon tagging
text, January 2017

Jet Substructure Studies with CMS Open Data
text, January 2017

Casimir Meets Poisson: Improved Quark/Gluon Discrimination with Counting Observables
text, January 2017