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Record W3022890809 · doi:10.1103/physrevb.101.144438

Hybrid modes of a cavity photon coupled with multiple magnons in ferromagnetic nanostructures

2020· article· en· W3022890809 on OpenAlexafffund
Zahra Haghshenasfard, M. G. Cottam

Bibliographic record

VenuePhysical review. B./Physical review. B · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMagnonCondensed matter physicsPhysicsFerromagnetismPhotonSpinsMicrowave cavityMicrowaveQuantum mechanics

Abstract

fetched live from OpenAlex

By using a microscopic dipole-exchange theory within a Green's function formalism, we study the coupling regime of microwave cavity photons to magnons in nanostructures. The ferromagnetic nanostructures considered are single- and double-layer films, nanowires with rectangular cross-sections, and one-dimensional magnonic crystal arrays of nanowires. In contrast with previous studies of magnon-photon hybrid systems, where either bulklike magnetic samples or macroscopic spheres or films were utilized, a discrete lattice of effective spins is employed to establish a microscopic theory for describing the dependence of the magnon frequencies on the wave vector. We explore hybrid systems derived from the discrete magnons in nanostructures and a selected microwave cavity photon, where strong photon-magnon coupling is expected. This may extend the functionality of the existing hybrid systems or may introduce new functionality. The dependence of the hybridized frequency modes on applied magnetic field are calculated and it is shown that the results are significantly modified compared with those for magnetic bulk materials. In particular, a series of anticrossing phenomena of the cavity mode and multiple magnon modes of ferromagnetic nanostructures are reported. Our findings are important for future applications in integrated hybrid systems for quantum magnonics.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.307
Teacher spread0.294 · 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.

Study designTheoretical or conceptual
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

Citations7
Published2020
Admission routes2
Has abstractyes

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