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Record W4399328074 · doi:10.1364/josab.521085

Soliton self-frequency shift prediction in amplifying fibers using the moment method

2024· article· en· W4399328074 on OpenAlexfundno aff
Robi Kormokar, Martin Rochette

Bibliographic record

VenueJournal of the Optical Society of America B · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSupercontinuumChirpSolitonMoment (physics)Nonlinear Schrödinger equationDispersion (optics)PhysicsPulse (music)Nonlinear systemWavelengthQuantum electrodynamicsComputational physicsOpticsQuantum mechanicsPhotonic-crystal fiber

Abstract

fetched live from OpenAlex

We formulate moment equations that quantify the soliton self-frequency shift in amplifying fibers. Soliton evolution is quantified in terms of energy, chirp, duration, delay, and central frequency and as a function of fiber properties of gain, dispersion, and nonlinearity and their wavelength-dependence. Results from the moment equations agree closely with results obtained from the nonlinear Schrodinger equation but without heavy computational resources requirements. Moment equations also have the great advantage of explicitly revealing the optimal initial pulse chirp that is required to induce maximum soliton self-frequency shift and energy conversion efficiency. The formulation is a simple and precise tool of utmost interest for the design of wavelength converters and supercontinuum sources based on soliton self-frequency shift.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.015
GPT teacher head0.299
Teacher spread0.285 · 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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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Same venueJournal of the Optical Society of America BSame topicAdvanced Fiber Laser TechnologiesFrench-language works237,207