Polarization fraction measurement in same-sign <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>W</mml:mi><mml:mi>W</mml:mi></mml:math> scattering using deep learning

Studying the longitudinally polarized fraction of ${W}^{\ifmmode\pm\else\textpm\fi{}}{W}^{\ifmmode\pm\else\textpm\fi{}}$ 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 $3000\text{ }\text{ }{\mathrm{fb}}^{\ensuremath{-}1}$.