Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks

TL;DR

This paper examines how variations in parton shower modeling affect deep neural network jet taggers for hadronic W-bosons at the LHC, highlighting significant differences and proposing a scale-invariance method to improve robustness.

Contribution

It investigates the impact of different event generator models on neural network performance and introduces a scale-invariance technique to enhance jet tagger robustness.

Findings

Differences up to fifty percent in background rejection across generators.

The proposed zooming method improves performance across jet transverse momenta.

Understanding jet physics features exploited by neural networks is crucial.

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.