Distinguishing $W'$ Signals at Hadron Colliders Using Neural Networks

TL;DR

This paper develops a neural network-based method to distinguish different $W'$ and charged scalar resonances at hadron colliders, addressing the challenge of four-fold ambiguity in proton-proton collision data.

Contribution

It introduces a multi-class neural network classifier trained on 2D kinematic histograms, improving the ability to identify resonance types compared to traditional methods.

Findings

Multi-class neural network classifier outperforms traditional hypothesis tests.

Using 1-jet processes allows generalization to multiple variable pairs.

Neural network approach effectively probes properties of charged resonances.

Abstract

We investigate a neural network-based hypothesis test to distinguish different $W'$ and charged scalar resonances through the $\ell+\require{cancel}\cancel{E}_T$ channel at hadron colliders. This is traditionally challenging due to a four-fold ambiguity at proton-proton colliders, such as the Large Hadron Collider. Of the neural network approaches we studied, we find a multi-class classifier based on a fully-connected neural network trained upon 2D histograms made from kinematic variables of the final state $\ell$ to be the most powerful. Furthermore, by considering the 1-jet processes, we demonstrate that one can generalize to multiple $2D$ histograms to represent different variable pairs. Finally, as a comparison to traditional approaches, we compare our method with Bayesian hypothesis testing and discuss the pros and cons of each approach. The neural network scheme presented in this paper is a powerful tool that can help probe the properties of charged resonances.