Polarization fraction measurement in same-sign WW scattering using deep learning

Abstract

Studying the longitudinally polarized fraction of $W^{\pm }{W}^{\pm }$ scattering at the LHC is crucial to examine the unitarization mechanism of the vector boson scattering amplitude through Higgs and possible new physics. We apply here for the first time a Deep Neural Network classification to extract the longitudinal fraction. Based on fast simulation implemented with the Delphes framework, significant improvement from a deep neural network is found to be achievable and robust over all dijet mass region. A conservative estimation shows that a high significance of four standard deviations can be reached with the High-Luminosity LHC designed luminosity of 3000 $f{b}^{-1}$

4 pages, 5 figures, updated draft to match published version

References in corpus (12)

• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• DELPHES 3, A modular framework for fast simulation of a generic collider experiment
• Searching for Exotic Particles in High-Energy Physics with Deep Learning
• TMVA - Toolkit for Multivariate Data Analysis
• Boosted Decision Trees as an Alternative to Artificial Neural Networks for Particle Identification
• Probing color-singlet exchange in $Z+2$-jet events at the LHC
• Observation of electroweak production of same-sign W boson pairs in the two jet and two same-sign lepton final state in proton-proton collisions at $\sqrt{s}=$ 13 TeV
• Enhanced Higgs to ${\tau }^{+}{\tau }^{-}$ Searches with Deep Learning
• Resonances and Unitarity in Weak Boson Scattering at the LHC
• Determination of the $WW$ polarization fractions in $pp\to W^{\pm }{W}^{\pm }jj$ using a deep machine learning technique
• The W_L W_L scattering at the LHC: improving the selection criteria
• High Energy WW Scattering at the LHC with the Matrix Element Method

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• Extract the energy scale of anomalous $\gamma \gamma \to W^{+}{W}^{-}$ scattering in the vector boson scattering process using artificial neural networks
• MLAnalysis: An open-source program for high energy physics analyses
• Deep Neural Network application: Higgs boson CP state mixing angle in H to tau tau decay and at LHC
• Polarization measurement for the dileptonic channel of $W^{+}{W}^{-}$ scattering using generative adversarial network
• Class Imbalance Techniques for High Energy Physics
• Amplitude-assisted tagging of longitudinally polarised bosons using wide neural networks
• Detecting anomaly in vector boson scattering
• Longitudinally polarized ZZ scattering at the Muon Collider
• Sensitivity to longitudinal vector boson scattering and doubly-charged Higgs boson production in ${\mathbf{W}}^{\pm }{\mathbf{W}}^{\pm }\mathbf{jj}$ at future hadron colliders
• A quantum machine learning classifier to search for new physics