Microscopic electronic configurations after ultrafast magnetization dynamics
I. L. M. Locht (1), I. Di Marco (1), S. Garnerone (2), A. Delin (1 and, 3, 4), M. Battiato (1, 5) ((1) Dept. of Physics, Astronomy, Uppsala, University, Uppsala, Sweden, (2) Institute for Quantum Computing, University, of Waterloo, Waterloo, Canada, (3) Department of Materials

TL;DR
This paper models the electronic and magnetic configurations of ferromagnetic iron after ultrafast magnetization changes, linking theoretical predictions to experimental T-MOKE data to support the occurrence of ultrafast magnetization increase.
Contribution
It introduces a model based on partial thermal equilibrium to predict magnetic configurations after ultrafast magnetization changes, providing new insights into ultrafast magnetization dynamics.
Findings
Qualitative differences in magnetic configurations for increased vs. decreased magnetization
Good agreement between theoretical predictions and experimental T-MOKE data
Support for the existence of ultrafast magnetization increase in ferromagnetic Fe
Abstract
We provide a model for the prediction of the electronic and magnetic configurations of ferromagnetic Fe after an ultrafast decrease or increase of magnetization. The model is based on the well-grounded assumption that, after the ultrafast magnetization change, the system achieves a partial thermal equilibrium. With statistical arguments it is possible to show that the magnetic configurations are qualitatively different in the case of reduced or increased magnetization. The predicted magnetic configurations are then used to compute the dielectric response at the 3p (M) absorption edge, which can be related to the changes observed in the experimental T-MOKE data. The good qualitative agreement between theory and experiment offers a substantial support to the existence of an ultrafast increase of magnetisation, which has been fiercely debated in the last years.
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.
