Poleval: A Python package for HAXPES analysis

TL;DR

Poleval is a Python toolbox that streamlines the analysis, visualization, and interpretation of XPS data, promoting reproducibility and collaborative research in surface analysis experiments.

Contribution

It introduces a comprehensive, notebook-based software for efficient, reproducible, and collaborative XPS data analysis with advanced fitting and quantitative capabilities.

Findings

Enables quick processing and visualization of XPS data.

Provides robust fitting routines for groups of spectra.

Includes methods for estimating adsorbate layer thickness.

Abstract

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.