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Record W3089695687 · doi:10.1088/1367-2630/abbc2a

Gratings with longitudinal variations in coupling coefficients: super-efficiency and unidirectionality in distributed feedback Raman fiber lasers

2020· article· en· W3089695687 on OpenAlexafffund
Amirhossein Tehranchi, Raman Kashyap

Bibliographic record

VenueNew Journal of Physics · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversité de MontréalPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsOpticsLaserCoupling coefficient of resonatorsLasing thresholdFiber laserCoupling (piping)Laser power scalingSlope efficiencyOptoelectronicsMaterials scienceResonator

Abstract

fetched live from OpenAlex

Abstract We propose a technique to design highly-efficient and -unidirectional DFB Raman fiber lasers based on the engineering of the grating’s coupling coefficient including a π -phase shift position in the fiber. For this purpose, first the ideal intra-cavity signal powers for different pump power levels are determined for given fiber lengths. Then, the sum and difference between the counter-propagating wave intensities at each small segment within fiber lengths are calculated resulting in determining the ideal grating’s coupling functions for co- and contra-directional-pumping. The steady-state behavior of the laser using realistic parameters is finally simulated for modified coupling functions considering the Kerr nonlinearity. For a 10 W co-directional-pumped, ∼1 m long single-mode super-efficient DFB, a ∼50% increase in the laser efficiency, more than 44 dB reduction in contra-directional lasing power, ∼15 times drop in the peak power of intra-cavity signal and ∼38% decrease in the unused-pump power are found, compared to those in a standard DFB with the same coupling-length product. Although an enhanced nonlinear refractive index due to thermal gradient reduces the output power of such lasers, it is shown that the super-efficient laser presents a better performance than the standard one, under such conditions.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score0.465

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)0.0000.000

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 head0.017
GPT teacher head0.229
Teacher spread0.212 · 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 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

Citations4
Published2020
Admission routes2
Has abstractyes

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