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Record W3199090674 · doi:10.1063/5.0057999

Theoretical analysis of backscattering in hollow-core antiresonant fibers

2021· article· en· W3199090674 on OpenAlexafffund
Eric Numkam Fokoua, Vincent Michaud-Belleau, Jérôme Genest, Radan Slavı́k, Francesco Poletti

Bibliographic record

VenueAPL Photonics · 2021
Typearticle
Languageen
FieldEngineering
TopicPhotonic Crystal and Fiber Optics
Canadian institutionsUniversité Laval
FundersEngineering and Physical Sciences Research CouncilNatural Sciences and Engineering Research Council of CanadaRoyal Academy of Engineering
KeywordsScatteringRayleigh scatteringCore (optical fiber)FiberOpticsMaterials scienceWavelengthScalingPhysicsGeometryComposite materialMathematics

Abstract

fetched live from OpenAlex

We present a theoretical analysis into the fundamental physical mechanisms contributing to backscattering in hollow-core antiresonant fibers. We consider Rayleigh scattering originating from the hollow regions of the fiber, which may be filled with gases, Rayleigh scattering from within the glass membranes, as well as the contribution from scattering at the glass surfaces. We derive expressions for the backscattering coefficient into any specified guided mode for an arbitrary excitation. These lead to general scaling relations with the core size and wavelength, which are found to be the key parameters for backscattering, regardless of the exact antiresonant geometry. For a nested antiresonant hollow-core fiber with a core diameter of 35 µm, the only antiresonant fiber geometry for which the experimental data are available in the literature, we find that the surface-scattering limited backscattering coefficient of the fundamental mode into itself is nearly 40 dB below that of a single mode fiber, in good agreement with recently published measurements.

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.220
Teacher spread0.211 · 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

Citations46
Published2021
Admission routes2
Has abstractyes

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