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Machine-learning approach to understanding ultrafast carrier dynamics in the three-dimensional Brillouin zone of PtBi2

Title: Machine-learning approach to understanding ultrafast carrier dynamics in the three-dimensional Brillouin zone of PtBi2
Authors: Majchrzak, Paulina; Sanders, Charlotte; Zhang, Yu; Kuibarov, Andrii; Suvorov, Oleksandr; Springate, Emma; Kovalchuk, Iryna; Aswartham, Saicharan; Shipunov, Grigory; Büchner, Bernd; Yaresko, Alexander; Borisenko, Sergey; Hofmann, Philip
Contributors: University of St Andrews.School of Physics and Astronomy
Publication Year: 2025
Collection: University of St Andrews: Digital Research Repository
Subject Terms: QC Physics; DAS; MCC; QC
Description: Funding: This work was supported by the Independent Research Fund Denmark (Grants No. 1026-00089B and No. 4258-00002B) and Novo Nordisk Foundation (Grant No. NFF23OC0085585). S.A. acknowledges DFG through AS 523/4-1. ; Using time- and angle-resolved photoemission spectroscopy, we examine the unoccupied electronic structure and electron dynamics of the type-I Weyl semimetal PtBi2. Using the ability to change the probe photon energy over a wide range, we identify the predicted Weyl points in the unoccupied three-dimensional band structure and we discuss the effect of k⊥ broadening in the normally unoccupied states. We characterize the electron dynamics close to the Weyl points and in other parts of three-dimensional Brillouin zone using k-means, an unsupervised machine-learning technique. This reveals distinct differences—in particular, that the electron dynamics close to the Weyl points are slower than in Brillouin zone regions close to the bulk Fermi surface. ; Peer reviewed
Document Type: article in journal/newspaper
File Description: application/pdf
Language: English
Relation: Physical Review Research; 329385136; https://hdl.handle.net/10023/33249
DOI: 10.1103/PhysRevResearch.7.013025
Availability: https://hdl.handle.net/10023/33249; https://doi.org/10.1103/PhysRevResearch.7.013025
Rights: © 2025 The Author(s). Published under the terms of the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/). Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.
Accession Number: edsbas.27C09A31
Database: BASE