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Record W7019669160

Implementing magnon-photon interaction using analytical and numerical techniques

2023· dissertation· en· W7019669160 on OpenAlexfundno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2023
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaResearch Manitoba
KeywordsPhotonCoupling (piping)QuantumReciprocity (cultural anthropology)InstabilityBounded functionMagnonCoalescence (physics)
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.535
Threshold uncertainty score0.754

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.273
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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