Regensburg 2025 – wissenschaftliches Programm
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O: Fachverband Oberflächenphysik
O 18: Poster Focus Session Ultrafast Electron Microscopy at the Space-Time Limit
O 18.1: Poster
Montag, 17. März 2025, 18:00–20:00, P2
Extending machine-learning-based band structure reconstruction into the time domain. — •Mirko Myksa1, Rui Patrick Xian1, Vincent Stimper2, Martin Wolf1, Ralph Ernstorfer1, and Laurenz Rettig1 — 1Fritz Haber Institute of the Max Planck Society, Berlin, Germany — 2Max Planck Institute for Intelligent Systems, Tübingen, Germany
Reliably extracting the electronic band dispersion from angle-resolved experimental photoemission (ARPES) data poses a challenging task, which often relies on specific line shape models and underlying assumptions, and thereby limiting a systematic and large-scale band structure extraction from volumetric ARPES data. For such purposes, we recently developed a band-structure reconstruction pipeline, including probabilistic machine learning and the associated data processing [1]. This pipeline shows an excellent performance on benchmarks for the reconstruction of three-dimensional photoemission (kx, ky, E) data from various materials. Here, the prospect for extending such analysis towards further dimensions such as pump-probe delay time-resolved ARPES will be discussed.
[1] R.P. Xian, et al., Nat. Comput. Sci. 3, 101 (2023)
Keywords: ARPES; Band structure; Machine-learning