MapsTorch: automatic differentiation for X‐ray fluorescence data analysis.
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| Title: | MapsTorch: automatic differentiation for X‐ray fluorescence data analysis. |
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| Authors: | Yin, Xiangyu1 (AUTHOR) xyin@anl.gov, Di, Zichao1 (AUTHOR), Antipova, Olga1 (AUTHOR), Chen, Si1 (AUTHOR), Jiang, Yi1 (AUTHOR), Glowacki, Arthur1 (AUTHOR) |
| Source: | Journal of Synchrotron Radiation. Jan2026, Vol. 33 Issue 1, p235-245. 11p. |
| Subjects: | X-ray fluorescence, Automatic differentiation, Data analysis, Open source software, Synchrotrons, Mathematical optimization, Spectrometry |
| Abstract: | X‐ray fluorescence (XRF) is a popular spectroscopy technique for elemental analysis. Spectrum fitting and parameter tuning are at the core of XRF analysis and are conventionally manually intensive, especially for synchrotron experiments involving large amounts of diverse samples. This work introduces the automatic differentiation (AD) technique to XRF and an open‐source package called MapsTorch. By transforming an analytical model of the XRF spectrum into a differentiable computation graph with AD, MapsTorch enables robust optimization of parameters and elemental intensities. We evaluate MapsTorch by conducting computational experiments on a large number of historical synchrotron XRF datasets and compare its performance with the currently practiced fitting tool NLopt. The results show that MapsTorch consistently achieves high‐quality fits and often leads to better fitting quality than NLopt, particularly in tasks such as initial spectrum fitting and elemental intensity refinement. The robust performance of MapsTorch paves the way for developing automated and high‐throughput XRF data analysis workflows to handle the increasing data volumes expected from next‐generation synchrotron facilities. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Synchrotron Radiation is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Abstract: | X‐ray fluorescence (XRF) is a popular spectroscopy technique for elemental analysis. Spectrum fitting and parameter tuning are at the core of XRF analysis and are conventionally manually intensive, especially for synchrotron experiments involving large amounts of diverse samples. This work introduces the automatic differentiation (AD) technique to XRF and an open‐source package called MapsTorch. By transforming an analytical model of the XRF spectrum into a differentiable computation graph with AD, MapsTorch enables robust optimization of parameters and elemental intensities. We evaluate MapsTorch by conducting computational experiments on a large number of historical synchrotron XRF datasets and compare its performance with the currently practiced fitting tool NLopt. The results show that MapsTorch consistently achieves high‐quality fits and often leads to better fitting quality than NLopt, particularly in tasks such as initial spectrum fitting and elemental intensity refinement. The robust performance of MapsTorch paves the way for developing automated and high‐throughput XRF data analysis workflows to handle the increasing data volumes expected from next‐generation synchrotron facilities. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 09090495 |
| DOI: | 10.1107/S160057752501032X |
