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Record W2953853365 · doi:10.1109/jlt.2019.2925588

Power Fluctuations and Random Lasing in Multiwavelength Brillouin Erbium-Doped Fiber Lasers

2019· article· en· W2953853365 on OpenAlexafffund
Amirhossein Tehranchi, Victor Lambin Iezzi, Raman Kashyap

Bibliographic record

VenueJournal of Lightwave Technology · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRandom lasers and scattering media
Canadian institutionsUniversité de MontréalPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsBrillouin scatteringLasing thresholdOpticsFiber laserPhysicsBrillouin zoneErbiumOptical amplifierLaserLaser power scalingAmplifierMaterials scienceOptoelectronics

Abstract

fetched live from OpenAlex

We experimentally and theoretically investigate the power fluctuations of the temporal interference signal produced by up to 5 Stokes orders generated by cascaded stimulated Brillouin scattering in a multiwavelength Brillouin erbium-doped fiber laser system with a 2.5-km-long intracavity fiber and erbium-doped fiber amplifier. Power fluctuations in such a system are due to the existence of several modes within the SBS gain bandwidth with the possibility of random hopping resulting in a chaotic temporal evolution of the pump and Stokes-wave powers and consequently the output signal power. Our simulations and statistical analyses show that at the threshold power resulting in the initial Stokes wave generation over round trips, the output signals have the maximum correlation over replicas; however, by increasing the power and producing more stokes waves, the correlation fades out leading to replica symmetry breaking and the system emits in a disordered manner and shows the signatures of a random laser.

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)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.003
GPT teacher head0.216
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 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

Citations20
Published2019
Admission routes2
Has abstractyes

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