Probing Topological Floquet States in WSe$_2$ using Circular Dichroism in Time- and Angle-Resolved Photoemission Spectroscopy
Michael Sch\"uler, Samuel Beaulieu

TL;DR
This study demonstrates how circular dichroism in photoelectron angular distributions can reveal Floquet topological states in WSe$_2$, overcoming challenges like scattering and LAPE interference, with predictions applicable to current experimental setups.
Contribution
It introduces a method to identify Floquet topological states via CDAD in WSe$_2$, combining ab initio modeling with photoemission matrix elements to distinguish Floquet features.
Findings
Floquet topological states produce characteristic CDAD signatures.
Predicted features are robust against scattering effects.
Method applicable to current experimental conditions.
Abstract
Observing signatures of light-induced Floquet topological states in materials has been shown to be very challenging. Angle-resolved photoemission spectroscopy (ARPES) is well suited for the investigation of Floquet physics, as it allows to directly probe the dressed electronic states of driven solids. Depending on the system, scattering and decoherence can play an important role, hampering the emergence of Floquet states. Another challenge is to disentangle Floquet side bands from laser-assisted photoemission (LAPE), since both lead to similar signatures in ARPES spectra. Here, we investigate the emergence of Floquet state in the transition metal dichalcogenide -WSe, one of the most promising systems for observing Floquet physics. We discuss how the Floquet topological state manifests in characteristic features in the circular dichroism in photoelectron angular distributions…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
Topics2D Materials and Applications · Machine Learning in Materials Science · Advanced Chemical Physics Studies
