Low-energy electron microscopy intensity-voltage data – factorization, sparse sampling, and classification

Low-energy electron microscopy (LEEM) taken as intensity-voltage (I-V) curves provides hyperspectral images of surfaces, which can be used to identify the surface type, but are difficult to analyze. Here, we demonstrate the use of an algorithm for factorizing the data into spectra and concentrations of characteristic components (FSC3) for identifying distinct physical surface phases. Importantly, FSC3 is an unsupervised and fast algorithm. As example data we use experiments on the growth of praseodymium oxide or ruthenium oxide on ruthenium single crystal substrates, both featuring a complex distribution of coexisting surface components, varying in both chemical composition and crystallographic structure. With the factorization result a sparse sampling method is demonstrated, reducing the measurement time by 1-2 orders of magnitude, relevant for dynamic surface studies. The FSC3 concentrations are providing the features for a support vector machine (SVM) based supervised classification of the types. Here, specific surface regions which have been identified structurally, via their diffraction pattern, as well as chemically by complementary spectro-microscopic techniques, are used as training sets. A reliable classification is demonstrated on both exemplary LEEM I-V datasets.

Research results are published at https://arxiv.org/abs/2203.12353

The data available represents the concentration maps obtained by the FSC3 in tiff format, together with the associated spectra as ascii. Similarly the results of the classification algorithm are available as tiff images, while the average concentration and spectra calculated over the training and testing regions are given as ascii data. The raw data are also given as tiff images, which can be used to test the FSC3 and classification algorithms (available at https://langsrv.astro.cf.ac.uk/HIA/HIA.html, and https://github.com/masiaf-cf/leem-svm-classify, respectively).

Research results based upon these data are pubished at https://doi.org/10.1111/jmi.13155