# Polarization fraction measurement in same-sign WW scattering using deep learning

## TL;DR

This paper introduces a deep neural network approach to measure the longitudinal polarization fraction in same-sign WW scattering at the LHC, demonstrating improved accuracy and robustness over traditional methods, and projecting high significance results with future collider data.

## Contribution

First application of deep neural networks for polarization measurement in same-sign WW scattering at the LHC, enhancing precision and robustness.

## Key findings

- Deep neural network improves measurement accuracy.
- Robustness across all dijet mass regions.
- Potential for four sigma significance at HL-LHC.

## Abstract

Studying the longitudinally polarized fraction of $W^\pm W^\pm$ 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 $fb^{-1}$