MUMOTT: a Python package for the analysis of multi-modal tensor tomography data.

Area of Science:

• Materials Science
• Nanotechnology
• Physics

Background:

• Small- and wide-angle X-ray scattering tensor tomography are advanced techniques for analyzing anisotropic nanostructures.
• Wider adoption of these methods is hindered by complex analysis procedures and algorithms.
• Synchrotron facilities are increasingly offering these powerful analytical capabilities.

Purpose of the Study:

• To introduce the MUMOTT package, a new Python-based software solution.
• To lower the barrier for using X-ray scattering tensor tomography.
• To improve the accessibility and usability of these advanced nanoscale characterization techniques.

Main Methods:

• Development of the MUMOTT package in Python.
• Implementation of just-in-time compilation for CPU and GPU acceleration of computationally intensive tasks.
• Focus on usability, extensibility, and high computational performance.

Main Results:

• The MUMOTT package provides efficient and user-friendly tools for tensor tomography data analysis.
• Demonstration of the package's computational performance and key features.
• The software is designed for easy integration into existing research workflows.

Conclusions:

• The MUMOTT package significantly lowers the threshold for adopting X-ray scattering tensor tomography.
• This tool aims to make advanced nanostructure analysis more accessible to a broader research community.
• Enhanced computational efficiency and usability are key benefits for researchers.