Polarization fraction measurement in ZZ scattering using deep learning

Abstract

Measuring longitudinally polarized vector boson scattering in the ZZ channel is a promising way to investigate unitarity restoration with the Higgs mechanism and to search for possible new physics. We investigated several deep neural network structures and compared their ability to improve the measurement of the longitudinal fraction ZLZL. Using fast simulation with the Delphes framework, a clear improvement is found using a previously investigated "particle-based" deep neural network on a preprocessed dataset and applying principle component analysis to the outputs. A significance of around 1.7 standard deviations can be achieved with the integrated luminosity of 3000 fb-1 that will be recorded at the High-Luminosity LHC. The technique developed in this article is also useful to other LHC analyses involving helicity fraction measurement.