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Record W4363673168 · doi:10.1103/physrevb.107.l140405

Dynamical backaction evading magnomechanics

2023· article· en· W4363673168 on OpenAlexafffund
C. A. Potts, Yunhu Huang, Victor A. S. V. Bittencourt, Silvia Viola Kusminskiy, J. P. Davis

Bibliographic record

VenuePhysical review. B./Physical review. B · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsUniversity of Alberta
FundersMax-Planck-GesellschaftDeutsche ForschungsgemeinschaftNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsPhysicsQuantum mechanicsLaser linewidthQuantum entanglementMechanical systemQuantum electrodynamicsClassical mechanicsPhotonQuantumLaser

Abstract

fetched live from OpenAlex

The interaction between magnons and mechanical vibrations dynamically modifies the properties of the mechanical oscillator, such as its frequency and decay rate. Known as dynamical backaction, this effect is the basis for many theoretical protocols, such as entanglement generation or mechanical ground-state cooling. However, dynamical backaction is also detrimental for specific applications. Here, we demonstrate the implementation of a triple-resonance cavity magnomechanical measurement that fully evades dynamical backaction effects. Through careful engineering, the magnomechanical scattering rate into the hybrid magnon-photon modes can be precisely matched, eliminating dynamical backaction damping. Backaction evasion is confirmed via the measurement of a drive-power-independent mechanical linewidth.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.374
Teacher spread0.355 · 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 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

Citations27
Published2023
Admission routes2
Has abstractyes

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