Poleval: A Python package for HAXPES analysis

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Hauptverfasser: Engel, Robin Yoël, Lömker, Patrick
Format: Preprint
Veröffentlicht: 2025
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author Engel, Robin Yoël
Lömker, Patrick
author_facet Engel, Robin Yoël
Lömker, Patrick
contents POLEVAL provides a software toolbox for collaborative, persistent and reproducible analysis of XPS experiments. It allows to treat, analyse and visualise the results of an extended experimental campaign in a single python notebook in a consistent manner. Managing experimental data in adequate objects enables experimentalists to process and analyse measurements in very few lines of code, so as to provide decision aids through online data analysis during e.g. beamtime experiments. The persistent and self-documentary style of the notebook-based analysis allows for easy communication of intermediate results and enables progressive refinements into publishable figures or exporting the results to other programs. The toolbox facilitates various routines for data treatment (normalization, cropping, etc.) and aggregation of spectra into groups to analyse trends. It also enables quantitative analysis with three major functions: First, normalization to the photoionization cross-section and probability of emission into the analyser cone allows for quantitative comparisons between intensities from different core levels. The integrated haxquantpy package allows easy retrieval of literature values 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 with prior knowledge, such as the equivalence of peak widths or positions between spectra, which enables for more consistent, and importantly, more confident fit results for sets of potentially noisy spectra. Third, a simple formalism to estimate the thickness of adsorbate layers based on the ratio between the substrate's and adsorbate's XPS signal is implemented.
format Preprint
id arxiv_https___arxiv_org_abs_2505_14194
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Poleval: A Python package for HAXPES analysis
Engel, Robin Yoël
Lömker, Patrick
Materials Science
Data Analysis, Statistics and Probability
POLEVAL provides a software toolbox for collaborative, persistent and reproducible analysis of XPS experiments. It allows to treat, analyse and visualise the results of an extended experimental campaign in a single python notebook in a consistent manner. Managing experimental data in adequate objects enables experimentalists to process and analyse measurements in very few lines of code, so as to provide decision aids through online data analysis during e.g. beamtime experiments. The persistent and self-documentary style of the notebook-based analysis allows for easy communication of intermediate results and enables progressive refinements into publishable figures or exporting the results to other programs. The toolbox facilitates various routines for data treatment (normalization, cropping, etc.) and aggregation of spectra into groups to analyse trends. It also enables quantitative analysis with three major functions: First, normalization to the photoionization cross-section and probability of emission into the analyser cone allows for quantitative comparisons between intensities from different core levels. The integrated haxquantpy package allows easy retrieval of literature values 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 with prior knowledge, such as the equivalence of peak widths or positions between spectra, which enables for more consistent, and importantly, more confident fit results for sets of potentially noisy spectra. Third, a simple formalism to estimate the thickness of adsorbate layers based on the ratio between the substrate's and adsorbate's XPS signal is implemented.
title Poleval: A Python package for HAXPES analysis
topic Materials Science
Data Analysis, Statistics and Probability
url https://arxiv.org/abs/2505.14194