Determination of the<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>W</mml:mi><mml:mi>W</mml:mi></mml:math>polarization fractions in<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>p</mml:mi><mml:mi>p</mml:mi><mml:mo stretchy="false">→</mml:mo><mml:msup><mml:mi>W</mml:mi><mml:mo>±</mml:mo></mml:msup><mml:msup><mml:mi>W</mml:mi><mml:mo>±</mml:mo></mml:msup><mml:mi>j</mml:mi><mml:mi>j</mml:mi></mml:math>using a deep machine learning technique

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.