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Real time measurements of ultrafast spontaneous modulation instability and rogue waves in optical fibre

2017· article· en· W2765182094 on OpenAlexaff
Mikko Närhi, Benjamin Wetzel, Cyril Billet, Jean-Marc Mérolla, Shanti Toenger, Thibaut Sylvestre, Roberto Morandotti, Goëry Genty, Frédéric Dias, John M. Dudley

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsUltrashort pulseRogue waveInstabilityModulation (music)OpticsOptical fiberPhysicsMaterials scienceOptoelectronicsMechanicsAcousticsLaserNonlinear systemQuantum mechanics

Abstract

fetched live from OpenAlex

Modulation instability (MI) is one of the most fundamental processes of nonlinear science, and develops in fibre optics when a weak perturbation on a continuous wave field experiences gain and evolves into strongly-localized “breather” structures. Although MI has been studied for decades, there continues to be intense interest in understanding its dynamics because, when triggered from noise, it generates high amplitude and statistically-rare “rogue waves” of importance in hydrodynamics and optics [1]. Due to experimental limitations, however, directly observing the ultrafast instability dynamics of noise-driven spontaneous MI in optics is extremely challenging and represents a major limitation in our ability to characterize this essential process. In this paper, we use an ultrafast time-lens magnifier system to perform direct measurements of real time temporal structures in MI. Our results show an extended series of transient high intensity breather pulses emerging from noise, and our statistical analysis allows the presence of long tails (rare events) to be readily seen.

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)0.0010.000

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 head0.021
GPT teacher head0.262
Teacher spread0.241 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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