Speaker
Description
Precision studies of $\tau^+\tau^-$ production at the Z pole provide a clean environment for investigating electroweak spin correlations and quantum information observables. Using archived LEP-1 data collected by the DELPHI experiment, the process $e^+e^- \to Z \to \tau^+\tau^-$ is well measured, but the presence of multiple neutrinos in $\tau$ decays limits reconstruction of the $\tau$-pair rest frame. This constrains the precision of spin-dependent measurements.
We present a machine-learning reconstruction method based on an event-level foundation model that predicts neutrino momenta using detector-level observables and kinematic constraints. The reconstructed $\tau^+\tau^-$ rest frame improves the resolution of spin-sensitive observables and enables a measurement of the $Z \to \tau^+\tau^-$ spin density matrix. These results provide a basis for quantum correlation studies using archived $e^+e^-$ collider data and demonstrate the applicability of machine-learning-based multi-neutrino reconstruction in precision electroweak analyses.