# Reconstruction of $\tau$ lepton pair invariant mass using an artificial neural network

## TL;DR

This paper presents a neural network approach to reconstruct the invariant mass of tau lepton pairs, improving accuracy and speed over existing algorithms, aiding Higgs and Z boson analyses.

## Contribution

The paper introduces a neural network method for di-tau mass reconstruction that outperforms traditional algorithms in bias, resolution, and computational efficiency.

## Key findings

- Neural network reduces bias in mass reconstruction.
- Improved resolution over previous algorithms.
- Faster computation time enhances analysis efficiency.

## Abstract

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.