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Record W4410092513 · doi:10.1103/physreva.111.053505

Kerr-enhanced optomechanical cooling in the unresolved-sideband regime

2025· article· en· W4410092513 on OpenAlexafffund
N. Diaz-Naufal, L. F. Deeg, D. Zoepfl, C. M. F. Schneider, Mathieu L. Juan, Gerhard Kirchmair, A. Metelmann

Bibliographic record

VenuePhysical review. A/Physical review, A · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsUniversité de Sherbrooke
FundersHORIZON EUROPE Framework ProgrammeDeutsche ForschungsgemeinschaftHorizon 2020 Framework ProgrammeCanada First Research Excellence FundAustrian Science FundNatural Sciences and Engineering Research Council of CanadaEuropean Commission
KeywordsSidebandPhysicsOpticsQuantum electrodynamicsQuantum mechanics

Abstract

fetched live from OpenAlex

Dynamical backaction cooling has been demonstrated to be a successful method for achieving the motional quantum ground state of a mechanical oscillator in the resolved-sideband regime, where the mechanical frequency is significantly larger than the cavity decay rate. Nevertheless, as mechanical systems increase in size, their frequencies naturally decrease, thus bringing them into the unresolved-sideband regime, where the effectiveness of the sideband cooling approach decreases. Here we demonstrate, however, that this cooling technique in the unresolved-sideband regime can be significantly enhanced by utilizing a nonlinear cavity as shown in the experimental work of Zoepfl []. The above arises due to the increased asymmetry between the cooling and heating processes, thereby improving the cooling efficiency. In addition, we show that injecting a squeezed vacuum into the nonlinear cavity paves the way to ground-state cooling of the mechanical mode. Notably, the required squeezing parameters are far less stringent than in the linear case, simplifying experimental implementation.

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)0.001
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.369
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 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

Citations5
Published2025
Admission routes2
Has abstractyes

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