Magnetic field reconstruction from proton radiography via physics-guided out-of-distribution filtering

Abstract

Reconstructing multidimensional vector fields from path-integrated projection data is a fundamental challenge in high-energy-density physics, particularly when experimental sources exhibit spectral broadening and shot-to-shot jitter. We present a physics-guided deep-learning framework that addresses this ill-posed inverse problem by formulating global reconstruction as an aggregation of local inference tasks. By training a neural network on single-particle trajectories in randomized uniform magnetic fields, we develop a “local solver” that demonstrates strong zero-shot transfer to previously unseen magnetohydrodynamic topologies. Central to addressing non-ideal laser-driven proton sources, we introduce a spectral out-of-distribution filter that rejects inputs outside the training energy envelope. By preventing extrapolation, the filter enables accurate reconstruction within the represented training domain while maintaining stable populated-cell reconstruction metrics across diverse spectral conditions. Furthermore, we introduce an a priori reconstruction-reliability indicator based on the in-distribution fraction of the source spectrum, which provides a practical estimate of reconstruction coverage before inference. This approach can be integrated with energy-resolved detector systems, such as stacked nuclear track detectors, establishing a practical and highly parallelizable framework for quantitative plasma diagnostics.