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Record W2795170411 · doi:10.1109/jqe.2018.2820218

Engineered <inline-formula> <tex-math notation="LaTeX">$\pi$ </tex-math> </inline-formula>-Phase-Shifted Fiber Bragg Gratings for Efficient Distributed Feedback Raman Fiber Lasers

2018· article· en· W2795170411 on OpenAlexafffund
Amirhossein Tehranchi, Sébastien Loranger, Raman Kashyap

Bibliographic record

VenueIEEE Journal of Quantum Electronics · 2018
Typearticle
Languageen
FieldEngineering
TopicPhotonic Crystal and Fiber Optics
Canadian institutionsUniversité de MontréalPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsPolytechnique MontréalUniversity of Essex
KeywordsFiber Bragg gratingMaterials scienceFiber laserRaman spectroscopyLaserOpticsPiOptical fiberFiberPhase (matter)OptoelectronicsPhysicsMathematicsQuantum mechanics

Abstract

fetched live from OpenAlex

Distributed feedback Raman fiber lasers with π-phase-shifted uniform gratings are modeled and simulated in the steady state, to optimize their performance. Using the parameters of realistic devices, it is found that the position change of the π-phase-shift in a constant-strength uniform grating has a significant impact on the laser performance including right and left-hand-side output power and emission frequency, and linewidth. Also, it is shown that the optimum phase-shift position to maximize the laser uni-directionality is dependent on pump power and fiber loss value. A new design approach based on an engineered π-phase-shifted step-like-strength uniform grating is presented demonstrating that even for a high-loss fiber (0.1 dB/m), the laser power and linewidth can be efficiently increased and decreased by ~25% and ~15%, respectively, for the same total device length and pumping condition, just by the proper choices of the phase-shift position and two coupling coefficients.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
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 head0.012
GPT teacher head0.245
Teacher spread0.233 · 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; both teacher heads agree on what is shown here.

Study designSimulation or modeling
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

Citations7
Published2018
Admission routes2
Has abstractyes

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