Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks

Machine learning methods incorporating deep neural networks have been the subject of recent proposals for new hadronic resonance taggers. These methods require training on a dataset produced by an event generator where the true class labels are known. However, training a network on a specific event...

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Published inarXiv.org
Main Authors Barnard, James, Dawe, Edmund Noel, Dolan, Matthew J, Rajcic, Nina
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 14.10.2016
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Online AccessGet full text
ISSN2331-8422
DOI10.48550/arxiv.1609.00607

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Abstract Machine learning methods incorporating deep neural networks have been the subject of recent proposals for new hadronic resonance taggers. These methods require training on a dataset produced by an event generator where the true class labels are known. However, training a network on a specific event generator may bias the network towards learning features associated with the approximations to QCD used in that generator which are not present in real data. We therefore investigate the effects of variations in the modelling of the parton shower on the performance of deep neural network taggers using jet images from hadronic W-bosons at the LHC, including detector-related effects. By investigating network performance on samples from the Pythia, Herwig and Sherpa generators, we find differences of up to fifty percent in background rejection for fixed signal efficiency. We also introduce and study a method, which we dub zooming, for implementing scale-invariance in neural network-based taggers. We find that this leads to an improvement in performance across a wide range of jet transverse momenta. Our results emphasise the importance gaining a detailed understanding what aspects of jet physics these methods are exploiting.
AbstractList Machine learning methods incorporating deep neural networks have been the subject of recent proposals for new hadronic resonance taggers. These methods require training on a dataset produced by an event generator where the true class labels are known. However, training a network on a specific event generator may bias the network towards learning features associated with the approximations to QCD used in that generator which are not present in real data. We therefore investigate the effects of variations in the modelling of the parton shower on the performance of deep neural network taggers using jet images from hadronic W-bosons at the LHC, including detector-related effects. By investigating network performance on samples from the Pythia, Herwig and Sherpa generators, we find differences of up to fifty percent in background rejection for fixed signal efficiency. We also introduce and study a method, which we dub zooming, for implementing scale-invariance in neural network-based taggers. We find that this leads to an improvement in performance across a wide range of jet transverse momenta. Our results emphasise the importance gaining a detailed understanding what aspects of jet physics these methods are exploiting.
Phys. Rev. D 95, 014018 (2017) Machine learning methods incorporating deep neural networks have been the subject of recent proposals for new hadronic resonance taggers. These methods require training on a dataset produced by an event generator where the true class labels are known. However, training a network on a specific event generator may bias the network towards learning features associated with the approximations to QCD used in that generator which are not present in real data. We therefore investigate the effects of variations in the modelling of the parton shower on the performance of deep neural network taggers using jet images from hadronic W-bosons at the LHC, including detector-related effects. By investigating network performance on samples from the Pythia, Herwig and Sherpa generators, we find differences of up to fifty percent in background rejection for fixed signal efficiency. We also introduce and study a method, which we dub zooming, for implementing scale-invariance in neural network-based taggers. We find that this leads to an improvement in performance across a wide range of jet transverse momenta. Our results emphasise the importance gaining a detailed understanding what aspects of jet physics these methods are exploiting.
Author Dawe, Edmund Noel
Rajcic, Nina
Barnard, James
Dolan, Matthew J
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BackLink https://doi.org/10.1103/PhysRevD.95.014018$$DView published paper (Access to full text may be restricted)
https://doi.org/10.48550/arXiv.1609.00607$$DView paper in arXiv
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Snippet Machine learning methods incorporating deep neural networks have been the subject of recent proposals for new hadronic resonance taggers. These methods require...
Phys. Rev. D 95, 014018 (2017) Machine learning methods incorporating deep neural networks have been the subject of recent proposals for new hadronic resonance...
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SubjectTerms Bosons
Large Hadron Collider
Machine learning
Neural networks
Physics - High Energy Physics - Experiment
Physics - High Energy Physics - Phenomenology
Quantum chromodynamics
Substructures
Training
Zooming
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Title Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks
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