Enhanced Visualization and Interpretation of XMCD‐PEEM Data Using SOM‐RPM Machine Learning

Abstract

Abstract Photoemission electron microscopy (PEEM) is a powerful technique for surface characterization that provides detailed information on the chemical and structural properties of materials at the nanoscale. In this study, the potential is explored using a machine learning algorithm called self‐organizing map with a relational perspective map (SOM‐RPM) for visualizing and analyzing complex PEEM‐generated datasets. The application of SOM‐RPM is demonstrated using synchrotron‐based X‐ray magnetic circular dichroism (XMCD)‐PEEM data acquired from a pyrrhotite sample. Traditional visualization approaches for XMCD‐PEEM data may not fully capture the complexity of the sample, especially in the case of heterogeneous materials. By applying SOM‐RPM to the XMCD‐PEEM data, a colored topographic map is created that represents the spectral similarities and dissimilarities among the pixels. This approach allows for a more intuitive and easily interpretable representation of the data without the need of data binning or spectral smoothing. The results of the SOM‐RPM analysis are compared to the conventional visualization approach, highlighting the advantages of SOM‐RPM in revealing features that are not readily observable in the conventional method. This study suggests that the SOM‐RPM approach can be used complimentarily for other PEEM‐based measurements, such as core level and valence band X‐ray photoelectron spectroscopy.

Keywords

• hyperspectral imaging
• machine learning
• pyrrhotite
• relation perspective imaging
• SOM‐RPM
• XMCD‐PEEM