Journal Article

PUBDB-2026-02000

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png POLEVAL: A Python package for HAXPES analysis

Engel, R.XFEL.EU*DESY* ; Lömker, P. (Corresponding author)Extern*

2026
JOSS [Erscheinungsort nicht ermittelbar]

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Please use a persistent id in citations: doi:10.21105/joss.08498  doi:10.3204/PUBDB-2026-02000

Abstract: POLEVAL provides a software toolbox for collaborative, persistent, and reproducible analysisof X-ray Photoelectron Spectroscopy (XPS), and particularly Hard X-ray Photo-ElectronSpectroscopy (HAXPES) experiments. It allows to treat, analyze, and visualize the resultsof an extended experimental campaign in a single Python notebook in a consistent manner.Managing experimental data in adequate objects enables experimentalists to process andanalyze measurements in very few lines of code, so as to provide decision aids through onlinedata analysis during e.g. beamtime experiments. The persistent and self-documentary style ofthe notebook-based analysis allows for easy communication of intermediate results and enablesprogressive refinements into publishable figures or exporting the results to other programs.The toolbox facilitates various routines for data treatment (normalization, cropping, etc.) andaggregation of spectra into groups to analyze trends. It also enables quantitative analysis withthree major functions:First, normalization to the photoionization cross-section and probability of emission into theanalyzer cone allows for quantitative comparisons between intensities from different core levels.The integrated haxquantpy package allows easy retrieval of literature values (Trzhaskovskaya& Yarzhemsky, 2018) for this purpose.Second, an extensive fitting functionality is implemented to treat groups of spectra together,rather than spectrum-by-spectrum. This grouping allows reinforcing the fit algorithm withprior knowledge, such as the equivalence of peak widths or positions between spectra, whichenables for more consistent, and importantly, more confident fit results for sets of potentiallynoisy spectra.Third, a simple formalism to estimate the thickness of adsorbate layers based on the ratiobetween the substrate’s and adsorbate’s XPS signal is implemented.

Note: cc-by

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Contributing Institute(s):

1. FS-PETRA (FS-PETRA)

Research Program(s):

1. 623 - Data Management and Analysis (POF4-623) (POF4-623)
2. SWEDEN-DESY - SWEDEN-DESY Collaboration (2020_Join2-SWEDEN-DESY) (2020_Join2-SWEDEN-DESY)

Experiment(s):

1. No specific instrument

Appears in the scientific report 2026

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Database coverage:
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