Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks

By J. Barnard, Edmund Noel Dawe, M. Dolan, Nina Rajcic. 2016

120 citations recorded by the source corpus.

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 data set produced by an event generator where the true class labels are known. However, this 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 modeling 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 50% 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 emphasize the importance of gaining a detailed understanding of what aspects of jet physics these methods are exploiting.

Subfields: Nuclear and High Energy Physics

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Citations in the corpus

QCD-aware recursive neural networks for jet physics (2017, methodology): “[5] when applied to the image pre-processed 4-momenta coming from DELPHES towers.”
Energy flow networks: deep sets for particle jets (2018, background): “One common event representation is to view events as images by treating the energy deposits in a calorimeter as pixel intensities [4–9].”