Autonomous microARPES
This work addresses the efficiency problem for researchers using microARPES in materials science by automating spatial and momentum-space searches, though it is incremental as it builds on existing Gaussian process regression techniques.
The researchers tackled the time-consuming scanning process in microscopic angle-resolved photoemission spectroscopy (microARPES) by implementing an autonomous protocol using Gaussian process regression to efficiently search for positions of interest based on photoemission intensity or sharp spectral features, resulting in a method that can be expanded with additional parameters or optimization criteria.
Angle-resolved photoemission spectroscopy (ARPES) is a technique used to map the occupied electronic structure of solids. Recent progress in X-ray focusing optics has led to the development of ARPES into a microscopic tool, permitting the electronic structure to be spatially mapped across the surface of a sample. This comes at the expense of a time-consuming scanning process to cover not only a three-dimensional energy-momentum ($E, k_z, k_y$) space but also the two-dimensional surface area. Here, we implement a protocol to autonomously search both $\mathbf{k}$- and real space in order to find positions of particular interest, either because of their high photoemission intensity or because of sharp spectral features. The search is based on the use of Gaussian process regression and can easily be expanded to include additional parameters or optimisation criteria. This autonomous experimental control is implemented on the SGM4 micro-focus beamline of the synchrotron radiation source ASTRID2.