Distinguishing $W^{\prime }$ Signals at Hadron Colliders Using Neural Networks

Abstract

We investigate a neural network-based hypothesis test to distinguish different $W^{\prime }$ and charged scalar resonances through the $\ell+\require{cancel}\cancel{E}_T$ channel at hadron colliders. This is traditionally challenging due to a four-fold ambiguity at proton-proton colliders, such as the Large Hadron Collider. Of the neural network approaches we studied, we find a multi-class classifier based on a fully-connected neural network trained upon 2D histograms made from kinematic variables of the final state $\ell$ to be the most powerful. Furthermore, by considering the 1-jet processes, we demonstrate that one can generalize to multiple $2D$ histograms to represent different variable pairs. Finally, as a comparison to traditional approaches, we compare our method with Bayesian hypothesis testing and discuss the pros and cons of each approach. The neural network scheme presented in this paper is a powerful tool that can help probe the properties of charged resonances.

arXiv comment44 pages, 22 figures

References in corpus (32)

• PYTHIA 6.4 Physics and Manual
• PYTHIA 6.2 Physics and Manual
• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• 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
• FeynRules 2.0 - A complete toolbox for tree-level phenomenology
• Parton distributions with LHC data
• Observation of Top Quark Production in Pbar-P Collisions
• Deep-learning Top Taggers or The End of QCD?
• The Machine Learning Landscape of Top Taggers
• Search for new resonances in mass distributions of jet pairs using 139 fb${}^{-1}$ of $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• Constraining Effective Field Theories with Machine Learning
• Search for high mass dijet resonances with a new background prediction method in proton-proton collisions at $\sqrt{s}=$ 13 TeV
• A Guide to Constraining Effective Field Theories with Machine Learning
• Search for a heavy charged boson in events with a charged lepton and missing transverse momentum from $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning
• Quark-Gluon Tagging: Machine Learning vs Detector
• Search for a W' boson decaying to a $\tau$ lepton and a neutrino in proton-proton collisions at $\sqrt{s}=$ 13 TeV
• A Review of Spin Determination at the LHC
• Search for diboson resonances in hadronic final states in 139 fb${}^{-1}$ of $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• Chiral Couplings of W' and Top Quark Polarization at the LHC
• Exploring SMEFT in VH with Machine Learning
• Boosted $W/Z$ Tagging with Jet Charge and Deep Learning
• A multi-dimensional search for new heavy resonances decaying to boosted WW, WZ, or ZZ boson pairs in the dijet final state at 13 TeV
• The Determination of the Helicity of $W^{\prime }$ Boson Couplings at the LHC
• Search for high-mass resonances in final states with a lepton and missing transverse momentum at $\sqrt{s}=$ 13 TeV
• Discovery in Drell-Yan Processes at the LHC
• Search for dijet resonances in events with an isolated charged lepton using $\sqrt{s}=13$ TeV proton-proton collision data collected by the ATLAS detector
• Machine Learning Accelerated Likelihood-Free Event Reconstruction in Dark Matter Direct Detection
• Determination of the Spin of New Resonances in Electroweak Gauge Boson Pair Production at the LHC

Cited by in corpus (4)

• E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once
• A method for approximating optimal statistical significances with machine-learned likelihoods
• Towards a method to anticipate dark matter signals with deep learning at the LHC
• Applying Machine Learning Techniques To Intermediate-Length Cascade Decays