Speaker
Description
Precision studies of $\tau^+\tau^-$ production in $e^+e^-$ collisions at LEP provide a clean environment for investigating spin correlations and quantum information observables. In the DELPHI experiment, the process $e^+e^- \to Z \to \tau^+\tau^-$ is well measured, but reconstruction of the $\tau^+\tau^-$ rest frame is challenged by the presence of multiple neutrinos in the final state. This limits the precision of spin-dependent measurements and quantum correlation studies.
We present a diffusion-based generative approach for reconstructing the $Z \to \tau^+\tau^-$ rest frame from detector-level inputs. The method performs conditional generation of neutrino momenta using visible objects and kinematic constraints, producing event-level kinematic hypotheses that can be used for further analysis. We demonstrate that this approach enhances the resolution of $\tau$-pair kinematics, enabling the accurate reconstruction of spin-sensitive observables. This work demonstrates a scalable strategy for multi-neutrino reconstruction at colliders and provides a computational foundation for quantum information studies using archived LEP data.