2018/12/31 by Junho Lee, Nicolas Chanon, A. Levin +8
Physics and Astronomy · #Algorithm #Artificial intelligence #Artificial neural network #Black Holes and Theoretical Physics #Computer science #Higgs boson #Large Hadron Collider #Luminosity #Machine learning #Optics #Particle physics #Particle physics theoretical and experimental studies #Physics #Physics beyond the Standard Model #Polarization (electrochemistry) #Quantum Chromodynamics and Particle Interactions #Quantum mechanics #Scattering #hep-ex #hep-ph
paper · pdf · doi:10.1103/physrevd.99.033004
published as Phys. Rev. D 99, 033004 (2019) · 4 pages, 5 figures, updated draft to match published version
openalex created_date 2018/12/22 · openalex publication_date 2019/02/08 · arxiv created 2019/02/18 · arxiv updated 2019/02/19 · openalex updated_date 2026/08/05
Studying the longitudinally polarized fraction of W^\ifmmode±\else\textpm\fiW^\ifmmode±\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 fb^\ensuremath-1.