Application of edge detection techniques to ARPES data

Edge detection and similar image analysis techniques are commonly used in computer vision but have not been fully realized for the purpose of ARPES data analysis. Without applying any Image analysis, the interpretation of ARPES data is left to the eyes of researchers and can be tricky and unreliable due to many sources of noise and distortion from the experimental processes, as a result, some of the finer, defining, details required to classify a material can be missed. By applying edge detection techniques, we are able to highlight key features such as distinct, clustered bands and other fine details that may otherwise have been obscured by noise and other experimental artefacts. Here we show the implementations of various image processing techniques applied to ARPES data and how they not only aid the interpretation of results, but can also be looked upon as stepping stones for better data processing techniques and potential automation of the classification of quantum materials through ARPES.