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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:
ORCiD logo [1];  [1]
  1. Harvard Univ., Cambridge, MA (United States)
Publication Date:
Research Org.:
Harvard Univ., Cambridge, MA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1483678
Grant/Contract Number:  
SC0013607
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of High Energy Physics (Online)
Additional Journal Information:
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.
Fraser, Katherine, & Schwartz, Matthew D. Jet charge and machine learning. United States. doi:10.1007/JHEP10(2018)093.
Fraser, Katherine, and Schwartz, Matthew D. Mon . "Jet charge and machine learning". United States. doi:10.1007/JHEP10(2018)093. https://www.osti.gov/servlets/purl/1483678.
@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)},
issn = {1029-8479},
number = 10,
volume = 2018,
place = {United States},
year = {2018},
month = {10}
}

Journal Article:
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Cited by: 1 work
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