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

Lee, Junho (Department of Physics and State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing 100871, China) ; Chanon, Nicolas (Institut de Physique Nucléaire de Lyon, Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3, Villeurbanne 69622, France) ; Levin, Andrew (Department of Physics and State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing 100871, China) ; Li, Jing (Department of Physics and State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing 100871, China) ; Lu, Meng (Department of Physics and State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing 100871, China) ; Li, Qiang (Department of Physics and State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing 100871, China) ; Mao, Yajun (Department of Physics and State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing 100871, China)

08 February 2019

Abstract: Studying the longitudinally polarized fraction of ${W}^{±}{W}^{±}$ 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 $3000\text{}\text{}{\mathrm{fb}}^{-1}$.

Published in: Physical Review D 99 (2019)