Regensburg 2025 – wissenschaftliches Programm

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O: Fachverband Oberflächenphysik

O 79: Ultrafast Electron Dynamics III

O 79.6: Vortrag

Donnerstag, 20. März 2025, 11:45–12:00, H2

Understanding the ultrafast electron dynamics and CDW transition in LaTe₃ using machine learning — •Gesa Siemann¹, Davide Curcio¹, Paulina Majchrzak¹, Charlotte Sanders², Jenny Rigden², Yu Zhang², Deepnarayan Biswas³, Leslie Schoop⁴, Emma Springate², and Philip Hofmann¹ — ¹Department of Physics and Astronomy, Aarhus University, DK — ²Central Laser Facility, Harwell, UK — ³Diamond Light Source, UK — ⁴Department of Chemistry, Princeton University, USA

The rare-earth tritelluride LaTe₃ hosts a unidirectional charge density wave (CDW) with a high transition temperature of 670 K. Recently, it has been suggested that exposing the system to a short light pulse not only suppresses this primary CDW but also induces a second CDW in the perpendicular direction¹. An open question is, how these structural dynamics affect the electronic structure, and if fingerprints of the second CDW can be found in corresponding data obtained by time- and angle-resolved photoemission spectroscopy. Here, we explore this question, studying the frequency-dependent coherent response of the system, and the time-dependent evolution of the Fermi surface topology, which we compare to predictions by a simple tight-binding model. We support our analysis using k-means clustering, a machine learning technique, in order to identify different dynamics throughout the Brillouin zone. This reveals varying relaxation times across the Fermi surface, as well as multiple frequencies that can be ascribed to coherent excitations. ¹A. Kogar et al., Nat. Phys. 16, 159*163 (2020).

Keywords: ultrafast electron dynamics; machine learning; CDW