Reconstruction of τ lepton pair invariant mass using an artificial neural network

The reconstruction of the invariant mass of $\tau$ lepton pairs is important for analyses containing Higgs and Z bosons decaying to $\tau^{+}\tau^{-}$, but highly challenging due to the neutrinos from the $\tau$ lepton decays, which cannot be measured in the detector. In this paper, we demonstrate how artificial neural networks can be used to reconstruct the mass of a di-$\tau$ system and compare this procedure to an algorithm used by the CMS Collaboration for this purpose. We find that the neural network output shows a smaller bias and better resolution of the di-$\tau$ mass reconstruction and an improved discrimination between a Higgs boson signal and the Drell-Yan background with a much shorter computation time.