  Imaging mechanisms in contact Kelvin Probe Force Microscopy (cKPFM) are
explored via information theory-based methods. Gaussian Processes are used to
achieve super-resolution in the cKPFM signal, effectively extrapolating across
the spatial and parameter space. Tensor matrix factorization is applied to
reduce the multidimensional signal to the tensor convolution of the scalar
functions that show clear trending behavior with the imaging parameters. These
methods establish a workflow for the analysis of the multidimensional data
sets, that can then be related to the relevant physical mechanisms. We also
provide an interactive Google Colab notebook (http://bit.ly/39kMtuR) that goes
through all the analysis discussed in the paper.
