Determination of the polarization fractions in using a deep machine learning technique
arXiv:1510.01691 · doi:10.1103/PhysRevD.93.094033
Abstract
The unitarization of the longitudinal vector boson scattering (VBS) cross section by the Higgs boson is a fundamental prediction of the Standard Model which has not been experimentally verified. One of the most promising ways to measure VBS uses events containing two leptonically-decaying same-electric-charge bosons produced in association with two jets. However, the angular distributions of the leptons in the boson rest frame, which are commonly used to fit polarization fractions, are not readily available in this process due to the presence of two neutrinos in the final state. In this paper we present a method to alleviate this problem by using a deep machine learning technique to recover these angular distributions from measurable event kinematics and demonstrate how the longitudinal-longitudinal scattering fraction could be studied. We show that this method doubles the expected sensitivity when compared to previous proposals.
arXiv comment7 pages, 4 figures, published in PRD
References in corpus (22)
- Deep Learning in Neural Networks: An Overview
- Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC
- Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC
- 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
- A Brief Introduction to PYTHIA 8.1
- DELPHES 3, A modular framework for fast simulation of a generic collider experiment
- Searching for Exotic Particles in High-Energy Physics with Deep Learning
- Neural Network Parametrization of Deep-Inelastic Structure Functions
- Evidence for Electroweak Production of in Collisions at TeV with the ATLAS Detector
- Study of vector boson scattering and search for new physics in events with two same-sign leptons and two jets
- Enhanced Higgs to Searches with Deep Learning
- Resonances and Unitarity in Weak Boson Scattering at the LHC
- High-Energy Vector Boson Scattering after the Higgs Discovery
- Exploring Higgs Triplet Models via Vector Boson Scattering at the LHC
- Novel Constraint on Parameter Space of the Georgi-Machacek Model by Current LHC Data
- New Physics Signals in Longitudinal Gauge Boson Scattering at the LHC
- The W_L W_L scattering at the LHC: improving the selection criteria
- Higgs constraints from vector boson fusion and scattering
- Vector boson scattering at the LHC. A study of the WW -> WW channels with the Warsaw cut
- Vector-Vector scattering at the LHC with two charged leptons and two neutrinos in the final state
- High Energy WW Scattering at the LHC with the Matrix Element Method
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- Measuring the anomalous quartic gauge couplings in the process at muon collider using artificial neural networks
- Comparing Traditional and Deep-Learning Techniques of Kinematic Reconstruction for polarisation Discrimination in Vector Boson Scattering
- Polarization fraction measurement in same-sign WW scattering using deep learning
- Polarization fraction measurement in ZZ scattering using deep learning
- NLO QCD corrections to polarised di-boson production in semi-leptonic final states
- Extract the energy scale of anomalous scattering in the vector boson scattering process using artificial neural networks
- MLAnalysis: An open-source program for high energy physics analyses
- Machine learning phases and criticalities without using real data for training
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- Class Imbalance Techniques for High Energy Physics
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- Deep-Learning in Search of Light Charged Higgs
- Sensitivity to longitudinal vector boson scattering and doubly-charged Higgs boson production in at future hadron colliders
- A universal neural network for learning phases and criticalities
- Sensitivity to longitudinal vector boson scattering in semileptonic final states at the HL-LHC