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Record W1597175091 · doi:10.1063/1.3183489

Nonlinear Optics in Confined Structures for a Better Understanding of Vacuum Field Fluctuations

2009· article· en· W1597175091 on OpenAlexaff
Jean Desforges, T. Ben-Messaoud, Martin Leblanc, Serge Gauvin, Mahi R. Singh, R. H. Lipson

Bibliographic record

VenueAIP conference proceedings · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsPhysicsResonatorNonlinear systemObservableNonlinear opticsWhispering-gallery waveSecond-harmonic generationOpticsQuantum fluctuationOptoelectronicsCoupling (piping)QuantumQuantum mechanicsLaserMaterials science

Abstract

fetched live from OpenAlex

The confinement of light between two highly reflective mirrors, the so‐called optical microcavities, can lead to very interesting novel effects. It is well known that the resonant recirculation of light inside a microcavity can be used to study the strong coupling of light waves with nonlinear materials. Under these confinement conditions, nonlinear optical effects like second harmonic generation, parametric fluorescence, and so on, become more easily observable. Microcavities can also be used to investigate vacuum quantum fluctuation, i.e. the temporary appearance of particle‐antiparticle pairs out of nothing allowed by the Heisenberg’s uncertainty principle. In such confined media, the fluctuating quantum states can be manipulated and intensified at specific wavelength by properly selecting the size of the microcavity resonator. In this work, we give an overview of the research done by our team using microcavities. We also discuss how these microcavities are fabricated using Bragg mirrors obtained from direct current magnetron sputtering.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.036
GPT teacher head0.281
Teacher spread0.246 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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