Speaker
Description
Precision tests of fundamental symmetries frequently rely on multi-stage experimental setups where distinct physical mechanisms shape a common observable via an intractable likelihood. Conditional invertible neural networks, trained on high-fidelity forward simulations, learn to reconstruct the full multidimensional posteriors over the parameter space directly from detector-level observables. Applied to ultracold neutron (UCN) storage experiments, which underlie searches for the CP-violating neutron permanent electric dipole moment (nEDM), the trained network accounts for a complex instrument response to disentangle competing capture and decay loss channels. As the next generation of nEDM experiments addresses the core challenge of limited statistics, generative inference offers a path to resolve new systematics via conditional summaries of particle-level simulations.