MétaCan
Menu
Back to cohort
Record W2997688168 · doi:10.1063/1.5144202

Dissipative couplings in cavity magnonics

2020· article· en· W2997688168 on OpenAlexafffund
Yi‐Pu Wang, C.‐M. Hu

Bibliographic record

VenueJournal of Applied Physics · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Manitoba
KeywordsMagnonDissipative systemMagnonicsPhysicsCoupling (piping)PhotonQuantum opticsMagnetismField (mathematics)DipoleQuantumCondensed matter physicsQuantum mechanicsFerromagnetismElectronMaterials science

Abstract

fetched live from OpenAlex

Cavity magnonics is an emerging field that studies the strong coupling between cavity photons and collective spin excitations such as magnons. This rapidly developing field connects some of the most exciting branches of modern physics, such as quantum information and quantum optics, with one of the oldest sciences on Earth, the magnetism. The past few years have seen a steady stream of exciting experiments that demonstrate novel magnon-based transducers and memories. Most of such cavity magnonic devices rely on coherent coupling that stems from the direct dipole–dipole interaction. Recently, a distinct dissipative magnon–photon coupling was discovered. In contrast to coherent coupling that leads to level repulsion between hybridized modes, dissipative coupling results in level attraction. It opens an avenue for engineering and harnessing losses in hybrid systems. This article gives a brief review of this new frontier. Experimental observations of level attraction are reviewed. Different microscopic mechanisms are compared. Based on such experimental and theoretical reviews, we present an outlook for developing open cavity systems by engineering and harnessing dissipative couplings.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.247
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations139
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Applied PhysicsSame topicMechanical and Optical ResonatorsFrench-language works237,207