Implementing magnon-photon interaction using analytical and numerical techniques
Bibliographic record
Abstract
The interaction between magnons and photons has significant implications for quantum information processing and in spintronics. In an electromagnetic cavity, wherein photons are confined, the photon-magnon coupling can be enhanced and controlled more effectively. In this thesis, we investigate a model system wherein the interaction between an antiferromagnetic domain wall and cavity photons occurs via the inverse Faraday effect, resulting in a coupling analogous to optomechanical coupling. The coupled dynamics leads to the emergence of level attraction, a term used to describe the coalescence of two distinct eigenmodes in a region bounded by exceptional points. We reveal the impact of the Dzyaloshinskii-Moriya Interaction (DMI) on the coupled dynamics of domain walls to cavity photons. We demonstrate that the presence of DMI can affect the coupling between magnons and photons, depending on the geometry of the system. This finding has potential applications in the control and detection of DMI in antiferromagnetic materials. Using a different formalism, we provide a demonstration that the phenomenon of level attraction can arise in systems characterized by instability under non-equilibrium conditions. A formalism for characterizing the linear response of driven magnonic systems has been developed and we show that results can be reproduced using numerical micromagnetics. The developed theory facilitates the study of cavity magnonics using numerical micromagnetics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".