2025/06/02 by Gesa-R. Siemann, Meyer, Tami C., Paulina Majchrzak +14
Materials Science · Physics and Astronomy · #Electron and X-Ray Spectroscopy Techniques #FOS: Physical sciences #Machine Learning in Materials Science #Nuclear Physics and Applications #Strongly Correlated Electrons (cond-mat.str-el)
paper · pdf · doi:10.48550/arxiv.2506.02137
openalex publication_date 2025/06/02 · openalex created_date 2025/10/14 · openalex updated_date 2026/07/28
Time- and angle-resolved photoemission spectroscopy is a powerful technique for investigating the dynamics of excited carriers in quantum materials. Typically, data analysis proceeds via the inspection of time distribution curves (TDCs), which represent the time-dependent photoemission intensity in a region of interest -- often chosen somewhat arbitrarily -- in energy-momentum space. Here, we employ k-means, an unsupervised machine learning technique, to systematically investigate trends in TDC line shape for quasi-free-standing monolayer graphene and for a simple analytical model. Our analysis reveals how finite energy and time resolution can affect the TDC line shape. We discuss how this can be taken into account in a quantitative analysis, and under what conditions the time-dependent photoemission intensity after laser excitation can be approximated by a simple exponential decay.