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 $fb^{-1}$
Highlights
Studying the longitudinally polarized fraction of WÆWÆ scattering at the LHC is crucial to examine the unitarization mechanism of the vector boson scattering amplitude through Higgs and possible new physics
The High-Luminosity LHC (HL-LHC) will measure for the first time many novel processes predicted by standard model (SM), and study precisely especially those involving pure electroweak interactions such as vector boson scattering (VBS)
Same charge WÆWÆ scattering has been observed by CMS and ATLAS with a significance larger tphaffisffin1⁄4513stTanedVa,rdcodrreevsipaotinodnisn,gbtaoseadn on data collected at integrated luminosity of approximately 35.9 fb−1 [1,2]
Summary
Studying the longitudinally polarized fraction of WÆWÆ scattering at the LHC is crucial to examine the unitarization mechanism of the vector boson scattering amplitude through Higgs and possible new physics. The High-Luminosity LHC (HL-LHC) will measure for the first time many novel processes predicted by standard model (SM), and study precisely especially those involving pure electroweak interactions such as vector boson scattering (VBS). [7] applied a regression with a deep neural network (DNN) to recover the lepton angular distributions in the W boson rest frame, and shows that the expected accuracy can be improved by about a factor of two compared to the use of RpT .
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