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28 March 2024 |
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Article overview
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Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks | James Barnard
; Edmund Noel Dawe
; Matthew J. Dolan
; Nina Rajcic
; | Date: |
2 Sep 2016 | 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. | Source: | arXiv, 1609.0607 | Services: | Forum | Review | PDF | Favorites |
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