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Related Experiment Video

Updated: Jan 8, 2026

Quantifying X-Ray Fluorescence Data Using MAPS

Quantifying X-Ray Fluorescence Data Using MAPS

Published on: February 17, 2018

MapsTorch: automatic differentiation for X-ray fluorescence data analysis.

Xiangyu Yin¹, Zichao Di¹, Olga Antipova¹

¹Advanced Photon Source, Argonne National Laboratory, Argonne, IL 60439, USA.

Journal of Synchrotron Radiation

|December 17, 2025

Summary

MapsTorch utilizes automatic differentiation to automate X-ray fluorescence (XRF) spectrum fitting, improving elemental analysis efficiency. This open-source tool offers robust parameter optimization, enhancing data analysis for synchrotron experiments.

Keywords:

X-ray fluorescence automatic differentiation machine learning optimization

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Last Updated: Jan 8, 2026

Quantifying X-Ray Fluorescence Data Using MAPS

Quantifying X-Ray Fluorescence Data Using MAPS

Published on: February 17, 2018

$Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples$

Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples

Published on: June 19, 2018

Imaging Intermediate Filaments and Microtubules with 2-dimensional Direct Stochastic Optical Reconstruction Microscopy

Imaging Intermediate Filaments and Microtubules with 2-dimensional Direct Stochastic Optical Reconstruction Microscopy

Published on: March 6, 2018

Area of Science:

Analytical Chemistry
Materials Science
Spectroscopy

Background:

X-ray fluorescence (XRF) is crucial for elemental analysis.
Manual spectrum fitting in XRF is time-consuming, especially for large synchrotron datasets.

Purpose of the Study:

Introduce automatic differentiation (AD) to XRF analysis.
Develop an open-source package, MapsTorch, for automated XRF data processing.

Main Methods:

Transforming analytical XRF models into differentiable computation graphs using AD.
Implementing MapsTorch for robust optimization of spectral parameters and elemental intensities.
Evaluating MapsTorch against NLopt using historical synchrotron XRF datasets.

Main Results:

MapsTorch achieves high-quality XRF spectrum fits.
MapsTorch demonstrates superior or comparable fitting quality to NLopt.
The tool excels in initial spectrum fitting and elemental intensity refinement.

Conclusions:

MapsTorch offers a robust and efficient solution for XRF data analysis.
The AD technique streamlines complex spectral fitting processes.
MapsTorch facilitates automated, high-throughput analysis for next-generation synchrotron facilities.