Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks

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.

arXiv comment9 pages, 4 figures; v2: plots updated, references added

References in corpus (53)

• The NumPy array: a structure for efficient numerical computation
• The anti-k_t jet clustering algorithm
• An Introduction to PYTHIA 8.2
• FastJet user manual
• DELPHES 3, A modular framework for fast simulation of a generic collider experiment
• Event generation with SHERPA 1.1
• Herwig++ Physics and Manual
• Herwig 7.0 / Herwig++ 3.0 Release Note
• A parton shower algorithm based on Catani-Seymour dipole factorisation
• Jet substructure as a new Higgs search channel at the LHC
• Comix, a new matrix element generator
• Identifying Boosted Objects with N-subjettiness
• Soft Drop
• Jet Trimming
• Energy Correlation Functions for Jet Substructure
• Transverse-Momentum-Ordered Showers and Interleaved Multiple Interactions
• Jet substructure as a new Higgs search channel at the LHC
• Boosted objects: a probe of beyond the Standard Model physics
• Recombination Algorithms and Jet Substructure: Pruning as a Tool for Heavy Particle Searches
• Fat Jets for a Light Higgs
• Jet Substructure at the Tevatron and LHC: New results, new tools, new benchmarks
• Jet-Images -- Deep Learning Edition
• Jet Substructure Classification in High-Energy Physics with Deep Neural Networks
• A Convolutional Neural Network Neutrino Event Classifier
• New formalism for QCD parton showers
• Power Counting to Better Jet Observables
• Boosted objects and jet substructure at the LHC
• Les Houches 2015: Physics at TeV Colliders Standard Model Working Group Report
• Search for high-mass diboson resonances with boson-tagged jets in proton-proton collisions at $\sqrt{s}$ = 8 TeV with the ATLAS detector
• Search for dark matter in events with a hadronically decaying W or Z boson and missing transverse momentum in pp collisions at sqrt(s)=8 TeV with the ATLAS detector
• Jet Flavor Classification in High-Energy Physics with Deep Neural Networks
• Seeing in Color: Jet Superstructure
• Jet-Images: Computer Vision Inspired Techniques for Jet Tagging
• Coherent Parton Showers with Local Recoils
• Dipole Showers and Automated NLO Matching in Herwig++
• Performance of jet substructure techniques for large-R jets in proton-proton collisions at sqrt(s) = 7 TeV using the ATLAS detector
• Scale-invariant resonance tagging in multijet events and new physics in Higgs pair production
• Identification techniques for highly boosted W bosons that decay into hadrons
• Search for anomalous t t-bar production in the highly-boosted all-hadronic final state
• Multivariate discrimination and the Higgs + W/Z search
• Search for vector-like charge 2/3 T quarks in proton-proton collisions at sqrt(s) = 8 TeV
• Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure
• Identification of boosted, hadronically decaying W bosons and comparisons with ATLAS data taken at $\sqrt{s}=8$ TeV
• Measurement of the charged-particle multiplicity inside jets from $\sqrt{s}=8$ TeV $pp$ collisions with the ATLAS detector
• Finding physics signals with event deconstruction
• Jet Dipolarity: Top Tagging with Color Flow
• Antenna Showers with Hadronic Initial States
• Parton Shower Uncertainties with Herwig 7: Benchmarks at Leading Order
• Boosting Top Partner Searches in Composite Higgs Models
• Higgs Search by Neural Networks at LHC
• Investigation of Monte Carlo Uncertainties on Higgs Boson searches using Jet Substructure
• Tracking New Physics at the LHC and beyond
• Searching for supersymmetry scalelessly

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