View in EDS HTML Full Text PDF Full Text

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

Saved in:
Bibliographic Details
Title: MapsTorch: automatic differentiation for X‐ray fluorescence data analysis.
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
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 190718639
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: MapsTorch: automatic differentiation for X‐ray fluorescence data analysis.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Yin%2C+Xiangyu%22">Yin, Xiangyu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> xyin@anl.gov</i><br /><searchLink fieldCode="AR" term="%22Di%2C+Zichao%22">Di, Zichao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Antipova%2C+Olga%22">Antipova, Olga</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Si%22">Chen, Si</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiang%2C+Yi%22">Jiang, Yi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Glowacki%2C+Arthur%22">Glowacki, Arthur</searchLink><relatesTo>1</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Journal+of+Synchrotron+Radiation%22">Journal of Synchrotron Radiation</searchLink>. Jan2026, Vol. 33 Issue 1, p235-245. 11p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22X-ray+fluorescence%22">X-ray fluorescence</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+differentiation%22">Automatic differentiation</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Open+source+software%22">Open source software</searchLink><br /><searchLink fieldCode="DE" term="%22Synchrotrons%22">Synchrotrons</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Spectrometry%22">Spectrometry</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>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.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=190718639
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1107/S160057752501032X
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 11
        StartPage: 235
    Subjects:
      – SubjectFull: X-ray fluorescence
        Type: general
      – SubjectFull: Automatic differentiation
        Type: general
      – SubjectFull: Data analysis
        Type: general
      – SubjectFull: Open source software
        Type: general
      – SubjectFull: Synchrotrons
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: Spectrometry
        Type: general
    Titles:
      – TitleFull: MapsTorch: automatic differentiation for X‐ray fluorescence data analysis.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Yin, Xiangyu
      – PersonEntity:
          Name:
            NameFull: Di, Zichao
      – PersonEntity:
          Name:
            NameFull: Antipova, Olga
      – PersonEntity:
          Name:
            NameFull: Chen, Si
      – PersonEntity:
          Name:
            NameFull: Jiang, Yi
      – PersonEntity:
          Name:
            NameFull: Glowacki, Arthur
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Text: Jan2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 09090495
          Numbering:
            – Type: volume
              Value: 33
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Journal of Synchrotron Radiation
              Type: main
ResultId 1