MétaCan
Menu
Back to cohort
Record W3119737430 · doi:10.1364/optica.403087

Backscattering in antiresonant hollow-core fibers: over 40  dB lower than in standard optical fibers

2021· article· lv· W3119737430 on OpenAlexafffund
Vincent Michaud-Belleau, Eric Numkam Fokoua, Thomas D. Bradley, J. R. Hayes, Y. Chen, Francesco Poletti, David J. Richardson, Jérôme Genest, Radan Slavı́k

Bibliographic record

VenueOptica · 2021
Typearticle
Languagelv
FieldEngineering
TopicPhotonic Crystal and Fiber Optics
Canadian institutionsUniversité Laval
FundersEngineering and Physical Sciences Research CouncilEuropean Research CouncilNatural Sciences and Engineering Research Council of CanadaFonds de recherche du Québec – Nature et technologiesUniversity of Southampton
KeywordsCore (optical fiber)Materials scienceOptical fiberOpticsComposite materialPhysics

Abstract

fetched live from OpenAlex

The elastic backscattering of light in optical fiber is a fundamental phenomenon that sets the ultimate performance of several fiber systems such as gyroscopes and bidirectional transfer links. Until now, efforts to reduce the backscattering coefficient have yielded limited results, with the lowest value sitting at around − 76 d B / m in Ge-free silica-core fiber at 1.55 µm. Here, we present what we believe to be the first measurement of backscattering from a low-loss antiresonant hollow-core fiber and show that it is more than 40 dB below reported values in silica-core fiber and hollow-core photonic bandgap fiber. The record-low level of − 118 d B / m measured with our custom-built optical frequency-domain reflectometer is in good agreement with simulations in which we assume the scattering to originate from the intrinsic surface roughness. Our demonstration also shows that a tailored instrument can localize and quantify weak faults within a hollow-core fiber, enabling its detailed characterization.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.003

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 head0.239
Teacher spread0.225 · 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

Citations85
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueOpticaSame topicPhotonic Crystal and Fiber OpticsFrench-language works237,207