Comparing Traditional and Deep-Learning Techniques of Kinematic Reconstruction for polarisation Discrimination in Vector Boson Scattering

TL;DR

This paper compares traditional and deep-learning methods for reconstructing W boson kinematics to measure polarization in vector boson scattering, crucial for testing the Standard Model and exploring new physics.

Contribution

It introduces and evaluates advanced deep neural network techniques alongside traditional methods for W boson kinematic reconstruction in polarization measurements.

Findings

Deep learning methods outperform traditional techniques in reconstruction accuracy.

Neural networks improve the measurement precision of the longitudinal W boson fraction.

The study demonstrates the potential of AI in high-energy physics analyses.

Abstract

Measuring longitudinally polarised vector boson scattering in 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 W boson kinematics in leptonic decays with the focus on polarisation measurements. We investigated several approaches, from traditional ones up to advanced deep neural network structures, and we compared their ability to reconstruct the W boson reference frame and to consequently measure the longitudinal fraction W_L in both semi-leptonic and di-leptonic WW decay channels.