# Polarization fraction measurement in ZZ scattering using deep learning

## TL;DR

This paper explores deep learning techniques to measure the longitudinal polarization fraction in ZZ scattering, aiming to enhance the detection of unitarity restoration and new physics at the High-Luminosity LHC.

## Contribution

It demonstrates that a particle-based deep neural network combined with PCA significantly improves polarization measurement accuracy in simulated ZZ scattering data.

## Key findings

- Achieved approximately 1.7 sigma significance with 3000 fb-1 luminosity.
- Compared various neural network structures and identified the most effective approach.
- Utilized fast simulation with Delphes to validate the method.

## 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 Z_L Z_L. 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.