Angle Resolved Photoemission

Angle-resolved photoemission spectroscopy (ARPES) maps the electronic structure of materials by measuring the energy and momentum of emitted electrons. Current research heavily emphasizes automating data analysis, including developing machine learning techniques like autoencoders and variational autoencoders to denoise spectra, compress large datasets, and identify features of interest within the high-dimensional data. These advancements address the challenges posed by the increasing volume and complexity of ARPES data generated by modern instruments, ultimately accelerating materials discovery and characterization. The improved efficiency and automation facilitated by these methods are crucial for maximizing the scientific output of ARPES experiments.