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Record W4302009315 · doi:10.1364/oe.467714

All-optical polarimeter for laser Stokes vector measurement using self-induced nonlinear phase modulation

2022· article· en· W4302009315 on OpenAlexafffund
Ole Krarup, Chams Baker, Liang Chen, Xiaoyi Bao

Bibliographic record

VenueOptics Express · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsOpticsStokes parametersPhysicsLaserPolarimeterPolarizerPolarization (electrochemistry)SidebandLinear polarizationPhase modulationPolarimetryScatteringPhase noiseMicrowave

Abstract

fetched live from OpenAlex

This paper utilizes an analytical model of polarization dependent frequency sideband generation via the Kerr effect in a highly nonlinear fiber to determine the state of polarization (SOP) of a laser by all-optical means. Theoretical analysis shows that the power of the n th order sideband generated by the propagation of two lasers with distinct frequencies in the nonlinear medium is proportional to cos 2n(α/2), where α is the angle between the normalized Stokes vectors representing the SOPs of the lasers on the Poincaré sphere. By tailoring the SOP of one laser acting as a reference and experimentally measuring the power of the first order sideband, the SOP of the laser under test is recovered with an error smaller than 10.22° on the Poincaré sphere corresponding to 0.8% the sphere’s total area. Comparing the SOPs of two lasers without referencing them to fixed polarizers enables potential applications in remote environmental sensing, novel polarization division multiplexing schemes for enhanced telecommunication data rates, and scientific instrumentation.

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: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.055
GPT teacher head0.289
Teacher spread0.234 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

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