  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.
