Invariant mass reconstruction of heavy gauge bosons decaying to $\tau$ leptons using machine learning techniques

TL;DR

This paper explores machine learning methods to improve the invariant mass reconstruction of heavy gauge bosons decaying to tau leptons, addressing challenges posed by missing neutrinos and enhancing search sensitivity at the LHC.

Contribution

The study applies supervised and unsupervised neural network algorithms to reconstruct invariant masses in tau decay channels, offering a novel approach to improve detection sensitivity.

Findings

Machine learning techniques enhance mass reconstruction accuracy.

Neural networks improve sensitivity in heavy gauge boson searches.

Methods address challenges of missing neutrinos in tau decays.

Abstract

Many analyses are performed by the LHC experiments to search for heavy gauge bosons, which appear in several new physics models. The invariant mass reconstruction of heavy gauge bosons is difficult when they decay to $\tau$ leptons due to missing neutrinos in the final state. Machine learning techniques are widely utilized in experimental high-energy physics, in particular in analyzing the large amount of data produced at the LHC. In this paper, we study machine learning techniques such as supervised and unsupervised neural network algorithms to reconstruct the invariant mass of $Z^{\prime}~\rightarrow~\tau\tau$ and $W^{\prime}~\rightarrow~\tau\nu$ decays, which can improve the sensitivity of these searches.