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 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.