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Kerr Enhanced Backaction Cooling in Magnetomechanics

2023· article· en· W4317040047 on OpenAlexafffund
D. Zoepfl, Mathieu L. Juan, N. Diaz-Naufal, C. M. F. Schneider, L. F. Deeg, Aleksei Sharafiev, A. Metelmann, Gerhard Kirchmair

Bibliographic record

VenuePhysical Review Letters · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsUniversité de Sherbrooke
FundersHorizon 2020 Framework ProgrammeCanada First Research Excellence FundDeutsche ForschungsgemeinschaftAustrian Science FundEuropean Commission
KeywordsOptomechanicsPhysicsLaser coolingQuantumPhotonNonlinear systemMechanical systemPhononQuantum mechanicsLaserComputer science

Abstract

fetched live from OpenAlex

Optomechanics is a prime example of light matter interaction, where photons directly couple to phonons, allowing the precise control and measurement of the state of a mechanical object. This makes it a very appealing platform for testing fundamental physics or for sensing applications. Usually, such mechanical oscillators are in highly excited thermal states and require cooling to the mechanical ground state for quantum applications, which is often accomplished by using optomechanical backaction. However, while massive mechanical oscillators are desirable for many tasks, their frequency usually decreases below the cavity linewidth, significantly limiting the methods that can be used to efficiently cool. Here, we demonstrate a novel approach relying on an intrinsically nonlinear cavity to backaction-cool a low frequency mechanical oscillator. We experimentally demonstrate outperforming an identical, but linear, system by more than 1 order of magnitude. Furthermore, our theory predicts that with this approach we can also surpass the standard cooling limit of a linear system. By exploiting a nonlinear cavity, our approach enables efficient cooling of a wider range of optomechanical systems, opening new opportunities for fundamental tests and sensing.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.319
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.297
Teacher spread0.281 · 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 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

Citations45
Published2023
Admission routes2
Has abstractyes

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