  Recent development in angle-resolved photoemission spectroscopy (ARPES)
technique involves spatially resolving samples while maintaining the
high-resolution feature of momentum space. This development easily expands the
data size and its complexity for data analysis, where one of it is to label
similar dispersion cuts and map them spatially. In this work, we demonstrate
that the recent development in representational learning (self-supervised
learning) model combined with k-means clustering can help automate that part of
data analysis and save precious time, albeit with low performance. Finally, we
introduce a few-shot learning (k-nearest neighbour or kNN) in representational
space where we selectively choose one (k=1) image reference for each known
label and subsequently label the rest of the data with respect to the nearest
reference image. This last approach demonstrates the strength of the
self-supervised learning to automate the image analysis in ARPES in particular
and can be generalized into any science data analysis that heavily involves
image data.
