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Title: Boosted W and Z tagging with jet charge and deep learning

Abstract

We demonstrate that the classification of boosted, hadronically decaying, weak gauge bosons can be significantly improved over traditional cut-based and boosted decision tree-based methods using deep learning and the jet charge variable. We construct binary taggers for $$\textit{W}^+$$ vs $$\textit{W}^–$$ and $$\textit{Z}$$ vs $$\textit{W}$$ discrimination, as well as an overall ternary classifier for $$\textit{W}^+/\textit{W}^–/\textit{Z}$$ discrimination. Besides a simple convolutional neural network, we also explore a composite of two simple convolutional neural networks, with different numbers of layers in the jet $$p_T$$ and jet charge channels. We find that this novel structure boosts the performance particularly when considering the $$\textit{Z}$$ boson as a signal. The methods presented here can enhance the physics potential in Standard Model measurements and searches for new physics that are sensitive to the electric charge of weak gauge bosons.

Authors:
ORCiD logo; ORCiD logo; ;
Publication Date:
Research Org.:
Rutgers Univ., Piscataway, NJ (United States)
Sponsoring Org.:
USDOE Office of Science (SC); NVIDIA Corporation; Taiwan Ministry of Science and Technology (MOST); Comisión Nacional de Investigación Científica y Tecnológica (CONICYT); National Fund for Scientific and Technological Development (FONDECYT)
OSTI Identifier:
1605460
Alternate Identifier(s):
OSTI ID: 1802022
Grant/Contract Number:  
SC0010008; MOST-104-2628-M-002-014-MY4; 3190051
Resource Type:
Published Article
Journal Name:
Physical Review D
Additional Journal Information:
Journal Name: Physical Review D Journal Volume: 101 Journal Issue: 5; Journal ID: ISSN 2470-0010
Publisher:
American Physical Society
Country of Publication:
United States
Language:
English
Subject:
79 ASTRONOMY AND ASTROPHYSICS; Astronomy & Astrophysics; Physics

Citation Formats

Chen, Yu-Chen Janice, Chiang, Cheng-Wei, Cottin, Giovanna, and Shih, David. Boosted W and Z tagging with jet charge and deep learning. United States: N. p., 2020. Web. doi:10.1103/PhysRevD.101.053001.
Chen, Yu-Chen Janice, Chiang, Cheng-Wei, Cottin, Giovanna, & Shih, David. Boosted W and Z tagging with jet charge and deep learning. United States. https://doi.org/10.1103/PhysRevD.101.053001
Chen, Yu-Chen Janice, Chiang, Cheng-Wei, Cottin, Giovanna, and Shih, David. Tue . "Boosted W and Z tagging with jet charge and deep learning". United States. https://doi.org/10.1103/PhysRevD.101.053001.
@article{osti_1605460,
title = {Boosted W and Z tagging with jet charge and deep learning},
author = {Chen, Yu-Chen Janice and Chiang, Cheng-Wei and Cottin, Giovanna and Shih, David},
abstractNote = {We demonstrate that the classification of boosted, hadronically decaying, weak gauge bosons can be significantly improved over traditional cut-based and boosted decision tree-based methods using deep learning and the jet charge variable. We construct binary taggers for $\textit{W}^+$ vs $\textit{W}^–$ and $\textit{Z}$ vs $\textit{W}$ discrimination, as well as an overall ternary classifier for $\textit{W}^+/\textit{W}^–/\textit{Z}$ discrimination. Besides a simple convolutional neural network, we also explore a composite of two simple convolutional neural networks, with different numbers of layers in the jet $p_T$ and jet charge channels. We find that this novel structure boosts the performance particularly when considering the $\textit{Z}$ boson as a signal. The methods presented here can enhance the physics potential in Standard Model measurements and searches for new physics that are sensitive to the electric charge of weak gauge bosons.},
doi = {10.1103/PhysRevD.101.053001},
journal = {Physical Review D},
number = 5,
volume = 101,
place = {United States},
year = {2020},
month = {3}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1103/PhysRevD.101.053001

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Cited by: 3 works
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Works referenced in this record:

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


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

  • Macaluso, Sebastian; Shih, David
  • Journal of High Energy Physics, Vol. 2018, Issue 10
  • DOI: 10.1007/JHEP10(2018)121

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


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


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


Measurements of jet charge with dijet events in pp collisions at s = 8 $$ \sqrt{s}=8 $$ TeV
journal, October 2017

  • Sirunyan, A. M.; Tumasyan, A.; Adam, W.
  • Journal of High Energy Physics, Vol. 2017, Issue 10
  • DOI: 10.1007/JHEP10(2017)131

DELPHES 3: a modular framework for fast simulation of a generic collider experiment
journal, February 2014

  • de Favereau, J.; Delaere, C.; Demin, P.
  • Journal of High Energy Physics, Vol. 2014, Issue 2
  • DOI: 10.1007/JHEP02(2014)057

The Machine Learning landscape of top taggers
journal, January 2019


Jet-images — deep learning edition
journal, July 2016

  • de Oliveira, Luke; Kagan, Michael; Mackey, Lester
  • Journal of High Energy Physics, Vol. 2016, Issue 7
  • DOI: 10.1007/JHEP07(2016)069

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

  • Komiske, Patrick T.; Metodiev, Eric M.; Schwartz, Matthew D.
  • Journal of High Energy Physics, Vol. 2017, Issue 1
  • DOI: 10.1007/JHEP01(2017)110

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

  • Kasieczka, Gregor; Plehn, Tilman; Russell, Michael
  • Journal of High Energy Physics, Vol. 2017, Issue 5
  • DOI: 10.1007/JHEP05(2017)006

The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
journal, July 2014

  • Alwall, J.; Frederix, R.; Frixione, S.
  • Journal of High Energy Physics, Vol. 2014, Issue 7
  • DOI: 10.1007/JHEP07(2014)079

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

  • Bhattacherjee, Biplob; Mukherjee, Swagata; Sengupta, Rhitaja
  • Journal of High Energy Physics, Vol. 2019, Issue 11
  • DOI: 10.1007/JHEP11(2019)156

Jet charge and machine learning
journal, October 2018

  • Fraser, Katherine; Schwartz, Matthew D.
  • Journal of High Energy Physics, Vol. 2018, Issue 10
  • DOI: 10.1007/JHEP10(2018)093

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


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


Jet Charge at the LHC
journal, May 2013


Uncovering latent jet substructure
journal, September 2019


Jet substructure at the Large Hadron Collider
journal, December 2019


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


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


An introduction to PYTHIA 8.2
journal, June 2015

  • Sjöstrand, Torbjörn; Ask, Stefan; Christiansen, Jesper R.
  • Computer Physics Communications, Vol. 191
  • DOI: 10.1016/j.cpc.2015.01.024

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

  • Almeida, Leandro G.; Backović, Mihailo; Cliche, Mathieu
  • Journal of High Energy Physics, Vol. 2015, Issue 7
  • DOI: 10.1007/JHEP07(2015)086

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


CapsNets continuing the convolutional quest
journal, January 2020


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

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


Herwig++ physics and manual
journal, November 2008


A new method to distinguish hadronically decaying boosted Z bosons from W bosons using the ATLAS detector
journal, April 2016