Boosted $W/Z$ Tagging with Jet Charge and Deep Learning

TL;DR

This paper introduces deep learning techniques combined with jet charge to improve the classification of boosted weak gauge bosons, significantly enhancing the ability to distinguish between different boson types in high-energy physics experiments.

Contribution

It presents novel deep learning architectures, including a composite CNN, that outperform traditional methods in classifying boosted $W/Z$ bosons using jet charge information.

Findings

Deep learning models outperform traditional cut-based and BDT methods.

Composite CNN architecture improves discrimination especially for $Z$ bosons.

Enhanced classification can improve SM measurements and new physics searches.

Abstract

We demonstrate that the classification of boosted, hadronically-decaying weak gauge bosons can be significantly improved over traditional cut-based and BDT-based methods using deep learning and the jet charge variable. We construct binary taggers for $W^+$ vs. $W^-$ and $Z$ vs. $W$ discrimination, as well as an overall ternary classifier for $W^+$/$W^-$/$Z$ discrimination. Besides a simple convolutional neural network (CNN), we also explore a composite of two CNNs, with different numbers of layers in the jet $p_{T}$ and jet charge channels. We find that this novel structure boosts the performance particularly when considering the $Z$ boson as signal. The methods presented here can enhance the physics potential in SM measurements and searches for new physics that are sensitive to the electric charge of weak gauge bosons.