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Improved Intrapulse Raman Scattering Control via Asymmetric Airy Pulses

2015· article· en· W2010199362 on OpenAlexafffund
Yi Hu, Amirhossein Tehranchi, S. Wabnitz, Raman Kashyap, Zhigang Chen, Roberto Morandotti

Bibliographic record

VenuePhysical Review Letters · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsUniversité de MontréalPolytechnique MontréalInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsRaman scatteringOpticsPhysicsSolitonRaman spectroscopyPulse (music)LaserFrequency offsetOffset (computer science)Main lobeNonlinear systemQuantum mechanicsTelecommunicationsDetector

Abstract

fetched live from OpenAlex

We experimentally demonstrate the possibility of tuning the frequency of a laser pulse via the use of an Airy pulse-seeded soliton self-frequency shift. The intrinsically asymmetric nature of Airy pulses, typically featured by either leading or trailing oscillatory tails (relatively to the main lobe), is revealed through the nonlinear generation of both a primary and a secondary Raman soliton self-frequency shift, a phenomenon which is driven by the soliton fission processes. The resulting frequency shift can be carefully controlled by using time-reversed Airy pulses or, alternatively, by applying an offset to the cubic phase modulation used to generate the pulses. When compared with the use of conventional chirped Gaussian pulses, our technique brings about unique advantages in terms of both efficient frequency tuning and feasibility, along with the generation and control of multicolor Raman solitons with enhanced tunability. Our theoretical analysis agrees well with our experimental observations.

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 communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.014
GPT teacher head0.278
Teacher spread0.264 · 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

Citations58
Published2015
Admission routes2
Has abstractyes

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