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Record W4403240251 · doi:10.1038/s41467-024-52954-7

Energy-managed soliton fiber laser

2024· article· en· W4403240251 on OpenAlexfundno aff
Mostafa I. Mohamed, Aurélien Coillet, Philippe Grelu

Bibliographic record

VenueNature Communications · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsnot available
FundersMinistère de l'Education Nationale, de l'Enseignement Superieur et de la RechercheMinistère de l'Enseignement Supérieur, de la Recherche, de la Science et de la TechnologieAgence Nationale de la Recherche
KeywordsChirpUltrashort pulseFiber laserDissipative systemDissipative solitonDispersion (optics)LaserPhysicsPicosecondSolitonFiberFlexibility (engineering)Pulse (music)OpticsOptical fiberMulti-mode optical fiberOptoelectronicsMaterials scienceQuantum mechanicsNonlinear system

Abstract

fetched live from OpenAlex

Ultrafast fiber lasers constitute a flexible platform to investigate new solitary wave concepts. To surpass the low energy limitation of the conventional solitons generated in standard telecom fibers, successive breakthroughs have promoted the usage of an important frequency chirping within fiber oscillators. This lead to original solitary wave regimes such as stretched-pulse, all-normal-dispersion, and self-similar dynamics. We here revisit ultrafast fiber lasers built from standard optical fibers featuring solely anomalous dispersion. We propose a new cavity design enhancing key dissipative effects with contained frequency chirping and demonstrate the generation of high energy pulses in the few-picoseconds regime. The involved intracavity dynamics blends conventional and dissipative soliton features in an unseen way with low- and high-energy propagation regions, allowing an increased flexibility and novel scalability prospects. The authors revisit ultrafast fiber lasers with anomalous dispersion to propose a new laser design enhancing dissipative effects. Their approach yields high-energy few-picosecond pulses, blending soliton features for improved flexibility and scalability.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.973
Threshold uncertainty score0.412

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 science0.0010.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.288
Teacher spread0.277 · 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 designNot applicable
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

Citations32
Published2024
Admission routes1
Has abstractyes

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