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Record W2347152252 · doi:10.1088/1367-2630/18/5/055003

Temporal characterization of a multi-wavelength Brillouin–erbium fiber laser

2016· article· en· W2347152252 on OpenAlexafffund
Victor Lambin Iezzi, Thomas Büttner, Amirhossein Tehranchi, Sébastien Loranger, Irina V. Kabakova, Benjamin J. Eggleton, Raman Kashyap

Bibliographic record

VenueNew Journal of Physics · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsPolytechnique Montréal
FundersFonds de recherche du Québec – Nature et technologiesAustralian Research CouncilNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsPhysicsOpticsBrillouin scatteringInstabilityBrillouin zoneWavelengthPicosecondOptical fiberPulse (music)LaserFiber laserErbiumMechanics

Abstract

fetched live from OpenAlex

ABSTRACT: This paper provides the first detailed temporal characterization of a multi-wavelength-Brillouin-erbium fiber laser (MWBEFL) by measuring the optical intensity of the individual frequency channels with high temporal resolution. It is found that the power in each channel is highly unstable due to the excitation of several cavity modes for typical conditions of operation. Also provided is the real-time measurements of the MWBEFL output power for two configurations that were previously reported to emit phase-locked picosecond pulse trains, concluded from their autocorrelation measurements. Real-time measurements reveal a high degree of instability without the formation of a stable pulse train. Finally, we model the MWBEFL using coupled wave equations describing the evolution of the Brillouin pump, Stokes and acoustic waves in the presence of stimulated Brillouin scattering, and the optical Kerr effect. A good qualitative consistency between the simulation and experimental results is evident, in which the interference signal at the output shows strong instability as well as the chaotic behavior due to the dynamics of participating pump and Stokes waves.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.661
Threshold uncertainty score0.387

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)0.0000.000

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 head0.249
Teacher spread0.232 · 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 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

Citations23
Published2016
Admission routes2
Has abstractyes

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