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Record W2729081316 · doi:10.1364/oe.25.015828

Single-mode SOA-based 1kHz-linewidth dual-wavelength random fiber laser

2017· article· en· W2729081316 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueOptics Express · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRandom lasers and scattering media
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsLaser linewidthFiber laserOpticsMaterials sciencePolarization-maintaining optical fiberDispersion-shifted fiberLaserFiber Bragg gratingDistributed feedback laserOptoelectronicsPhotonic-crystal fiberOptical fiberOptical amplifierFiber optic sensorPhysics

Abstract

fetched live from OpenAlex

Narrow-linewidth multi-wavelength fiber lasers are of significant interests for fiber-optic sensors, spectroscopy, optical communications, and microwave generation. A novel narrow-linewidth dual-wavelength random fiber laser with single-mode operation, based on the semiconductor optical amplifier (SOA) gain, is achieved in this work for the first time, to the best of our knowledge. A simplified theoretical model is established to characterize such kind of random fiber laser. The inhomogeneous gain in SOA mitigates the mode competition significantly and alleviates the laser instability, which are frequently encountered in multi-wavelength fiber lasers with Erbium-doped fiber gain. The enhanced random distributed feedback from a 5km non-uniform fiber provides coherent feedback, acting as mode selection element to ensure single-mode operation with narrow linewidth of ~1kHz. The laser noises are also comprehensively investigated and studied, showing the improvements of the proposed random fiber laser with suppressed intensity and frequency noises.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.258
Teacher spread0.239 · 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