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Record W2531244140 · doi:10.1038/srep35090

Transparency and tunable slow and fast light in a nonlinear optomechanical cavity

2016· article· en· W2531244140 on OpenAlexaff
Ling Li, Wenjie Nie, Aixi Chen

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsUniversity of Waterloo
FundersNatural Science Foundation of Jiangxi ProvinceNational Natural Science Foundation of China
KeywordsDegenerate energy levelsElectromagnetically induced transparencyPhysicsAtomic coherenceOptical parametric amplifierLight fieldExcited stateOptical parametric oscillatorCoherence (philosophical gambling strategy)AmplifierNonlinear opticsOptical switchNonlinear systemOptoelectronicsField (mathematics)Optical amplifierExcitationAbsorption (acoustics)OpticsAtomic physicsWavelengthLaserQuantum mechanics

Abstract

fetched live from OpenAlex

We investigate theoretically the optical response of the output field and the tunable slow and fast light in a nonlinear optomechanical cavity with a degenerate optical parametric amplifier (OPA) and a higher order excited atomic ensemble. Studies show that the higher-order-excitation atom which is similar to the degenerate OPA that acts as a nonlinear medium, induces an additional dip in absorption spectrum of the probe field. The coherence of the mechanical oscillator leads to split the peak in absorption in the probe field spectrum so that the phenomenon of optomechanically induced transparency (OMIT) is generated from the output probe field. In particular, the presence of nonlinearities with the degenerate OPA and the higher order excited atoms can affect significantly the width of the transparency windows, providing an additional flexibility for controlling optical properties. Furthermore, in the presence of the degenerate OPA, the optical-response properties for the probe field become phase-sensitive so that a tunable switch from slow to fast light can be realized.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.601
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.230
Teacher spread0.223 · 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.

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

Citations51
Published2016
Admission routes1
Has abstractyes

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