Determination of the $WW$ polarization fractions in $pp \to W^\pm W^\pm jj$ using a deep machine learning technique

TL;DR

This paper introduces a deep learning method to extract $W$ boson polarization information from complex collider events, enabling more precise tests of the Standard Model's predictions on vector boson scattering.

Contribution

The paper presents a novel deep machine learning approach to recover lepton angular distributions in $WW$ scattering events with neutrinos, improving sensitivity to polarization fractions.

Findings

Method doubles the expected sensitivity to longitudinal $WW$ scattering fraction

Successfully reconstructs angular distributions from measurable kinematics

Enhances experimental verification of the Standard Model predictions

Abstract

The unitarization of the longitudinal vector boson scattering (VBS) cross section by the Higgs boson is a fundamental prediction of the Standard Model which has not been experimentally verified. One of the most promising ways to measure VBS uses events containing two leptonically-decaying same-electric-charge $W$ bosons produced in association with two jets. However, the angular distributions of the leptons in the $W$ boson rest frame, which are commonly used to fit polarization fractions, are not readily available in this process due to the presence of two neutrinos in the final state. In this paper we present a method to alleviate this problem by using a deep machine learning technique to recover these angular distributions from measurable event kinematics and demonstrate how the longitudinal-longitudinal scattering fraction could be studied. We show that this method doubles the expected sensitivity when compared to previous proposals.