Cone beam neutron interferometry: from modeling to applications

TL;DR

This paper develops a k-space model for cone beam neutron phase-grating moire interferometers, enabling intuitive analysis, optimizing fringe visibility, and improving sample characterization, with validation against experiments.

Contribution

It generalizes the Fresnel scaling theorem to create a forward propagation model for PGMIs illuminated by cone beams, facilitating setup exploration and application development.

Findings

Model predictions agree well with experimental data.

Optimal fringe visibility occurs at Talbot moire locations.

Method to recover contrast at challenging autocorrelation lengths.

Abstract

Phase-grating moire interferometers (PGMIs) have emerged as promising candidates for the next generation of neutron interferometry, enabling the use of a polychromatic beam and manifesting interference patterns that can be directly imaged by existing neutron cameras. However, the modeling of the various PGMI configurations is limited to cumbersome numerical calculations and backward propagation models which often do not enable one to explore the setup parameters. Here we generalize the Fresnel scaling theorem to introduce a k-space model for PGMI setups illuminated by a cone beam, thus enabling an intuitive forward propagation model for a wide range of parameters. The interference manifested by a PGMI is shown to be a special case of the Talbot effect, and the optimal fringe visibility is shown to occur at the moire location of the Talbot distances. We derive analytical expressions for the contrast and the propagating intensity profiles in various conditions, and analyze the behaviour of the dark-field imaging signal when considering sample characterization. The model's predictions are compared to experimental measurements and good agreement is found between them. Lastly, we propose and experimentally verify a method to recover contrast at typically inaccessible PGMI autocorrelation lengths. The presented work provides a toolbox for analyzing and understanding existing PGMI setups and their future applications, for example extensions to two-dimensional PGMIs and characterization of samples with non-trivial structures.