Comparing traditional and deep-learning techniques of kinematic reconstruction for polarization discrimination in vector boson scattering

Abstract

Abstract Measuring longitudinally polarized vector boson scattering in $$\mathrm {WW}$$ WW channel is a promising way to investigate unitarity restoration with the Higgs mechanism and to search for possible physics beyond the Standard Model. In order to perform such a measurement, it is crucial to develop an efficient reconstruction of the full $$\mathrm {W}$$ W boson kinematics in leptonic decays with the focus on polarization measurements. We investigated several approaches, from traditional ones up to advanced deep neural network structures, and we compared their abilities in reconstructing the $$\mathrm {W}$$ W boson reference frame and in consequently measuring the longitudinal fraction $$\mathrm {W}_{\text {L}}$$ W L in both semi-leptonic and fully-leptonic $$\mathrm {WW}$$ WW decay channels.