Abstract
Hierarchical structures and heterogeneous materials are found in many natural and engineered systems including additive manufacturing, alternative energy, biology and polymer science. Though the structure-function relationship is important for developing more advanced materials, structural characterization over broad length scales often requires multiple complementary measurements. Neutron far-field interferometry aims to enable multi-scale characterization by combining the best of neutron imaging with small-angle neutron scattering (SANS) via dark-field imaging. The microstructure, nominally from 1 nm to 10 μm, is averaged over each volume element ~(50 μm)(3) in the sample, resulting in a 'tomographic SANS' measurement. Unlike in small-angle scattering, there are few analytical models to fit dark-field imaging data to extract properties of the microstructure. Fortunately, the dark field and SANS are related through a single Hankel transform. In this work, we discuss the development of a Python-based library, correlogram-tools, that makes use of existing small-angle scattering models and a numerical implementation of the Hankel transform to simulate dark-field interferometry data. We demonstrate how this software can be used to inform researchers of viable sample sets for interferometry experiments, analyze interferometry data, and simulate raw and reconstructed interferometry images for the training of more advanced segmentation models and analysis protocols.