A unified Bayesian framework for multi-diagnostic inversion in Z-pinch experiments

We present a unified Bayesian inversion framework for multi-diagnostic plasma analysis, demonstrated here on Z-pinch experiments. A shared Bayesian neural network (BNN) core couples with interchangeable physics-based forward models, so that one variational inference engine serves all diagnostics with built-in uncertainty quantification. Two modules are developed. (i) A two-angle Thomson scattering (TS) module that enforces a single self-consistent plasma state across viewing angles by modeling the full dynamic structure factor convolved with pre-characterized instrumental functions—unlike conventional analyses that treat each angle independently. (ii) A joint interferometry and Faraday rotation module that simultaneously constrains phase and magnetic field, replacing the standard sequential workflow in which phase-unwrapping errors propagate uncorrected into field estimates; a hybrid semi-global matching plus BNN approach resolves phase-branch ambiguities where conventional algorithms fail. On synthetic data with realistically modeled noise, the TS module achieves overall coefficient of determination R2>0.98 for ne, Te, Ti, and ion velocity, and satisfactory reconstruction of the electron velocity, which is inherently harder to infer. The interferometry module achieves phase mean absolute error (MAE) <0.14 rad over a ∼5π rad dynamic range and path-averaged magnetic-field MAE <7.2 T against ∼40 T peak values. The framework is validated on a Z-pinch experiment, yielding spatially resolved ne, Te, Ti, and flow velocities with full uncertainty quantification.