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 in | arXiv.org |
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Main Authors | , , , |
Format | Paper Journal Article |
Language | English |
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Cornell University Library, arXiv.org
14.10.2016
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ISSN | 2331-8422 |
DOI | 10.48550/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. |
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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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