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

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.