Big data spectromicroscopy: achieving new observables in ARPES from 2D surface maps*

Topological insulators (TIs) are bulk semiconductors that manifest spin-helical conducting surface states due to the topological bulk-boundary correspondence principle. Control and deeper understanding of the surface environment is thus of vital importance for long-term technological goals. In this talk, I will describe a data acquisition and analysis framework termed sparse big data (SBD) spectroscopy, in which one rapidly maps a sample surface to resolve the variation of electronic structure as a function of local environment. Leveraging the large data set enables one to realize the analog of a multi-dimensional doping series. I will present examples demonstrating that the fine ‘control’ this entails can be used to unlock new experimental observables such as a hybridization-like interplay between the surface and bulk electronic states in a TI. I will also touch on likely avenues for future development involving convolutional neural networks and new data visualization strategies.