Speaker
Description
Precision studies of $τ⁺τ⁻$ 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 $e⁺e⁻ → Z → τ⁺τ⁻$ is well measured, but the presence of multiple neutrinos in $τ$ decays limits reconstruction of the $τ$-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 $τ⁺τ⁻$ rest frame improves the resolution of spin-sensitive observables and enables a measurement of the $Z → τ⁺τ⁻$ 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.