# Autonomous Polycrystalline Material Decomposition for Hyperspectral Neutron Tomography

## TL;DR

This paper presents an autonomous algorithm for decomposing and localizing polycrystalline materials in hyperspectral neutron tomography, enabling non-destructive analysis of complex crystalline structures with high accuracy.

## Contribution

The paper introduces an autonomous material decomposition algorithm that estimates attenuation spectra and reconstructs 3D material volumes from hyperspectral neutron data, advancing non-destructive crystalline analysis.

## Key findings

- Accurately estimates attenuation spectra from simulated and experimental data.
- Successfully decomposes polycrystalline structures in 3D reconstructions.
- Demonstrates effectiveness on both simulated and real neutron tomography data.

## Abstract

Hyperspectral neutron tomography is an effective method for analyzing crystalline material samples with complex compositions in a non-destructive manner. Since the counts in the hyperspectral neutron radiographs directly depend on the neutron cross-sections, materials may exhibit contrasting neutron responses across wavelengths. Therefore, it is possible to extract the unique signatures associated with each material and use them to separate the crystalline phases simultaneously. We introduce an autonomous material decomposition (AMD) algorithm to automatically characterize and localize polycrystalline structures using Bragg edges with contrasting neutron responses from hyperspectral data. The algorithm estimates the linear attenuation coefficient spectra from the measured radiographs and then uses these spectra to perform polycrystalline material decomposition and reconstructs 3D material volumes to localize materials in the spatial domain. Our results demonstrate that the method can accurately estimate both the linear attenuation coefficient spectra and associated reconstructions on both simulated and experimental neutron data.