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

Leonard C Nielsen1, Mads Carlsen2, Sici Wang2,3

  • 1Department of Physics Chalmers University of Technology Gothenburg Sweden.

Journal of Applied Crystallography
|October 9, 2025
PubMed
Summary
This summary is machine-generated.

The MUMOTT package simplifies the analysis of X-ray scattering tensor tomography data for anisotropic nanostructures. This Python tool enhances accessibility and computational efficiency for researchers using synchrotron facilities.

Keywords:
reconstructionsmall-angle X-ray scatteringsoftwaretensor tomographywide-angle X-ray scattering

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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.