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Record W4317033659 · doi:10.1364/prj.473239

Classical imaging with undetected photons using four-wave mixing in silicon core fibers

2023· article· en· W4317033659 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePhotonics Research · 2023
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsUniversity of Toronto
FundersNorges ForskningsrådNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaEngineering and Physical Sciences Research CouncilJ. E. Sirrine Textile Foundation
KeywordsAlgorithmMaterials scienceComputer science

Abstract

fetched live from OpenAlex

Undetected-photon imaging allows for objects to be imaged in wavelength regions where traditional components are unavailable. Although first demonstrated using quantum sources, recent work has shown that the technique also holds with classical beams. To date, however, all the research in this area has exploited parametric down-conversion processes using bulk nonlinear crystals within free-space systems. Here, we demonstrate undetected-photon-based imaging using light generated via stimulated four-wave mixing within highly nonlinear silicon fiber waveguides. The silicon fibers have been tapered to have a core diameter of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m1"> <mml:mrow> <mml:mo form="prefix">∼</mml:mo> <mml:mn>915</mml:mn> <mml:mtext> </mml:mtext> <mml:mi>nm</mml:mi> </mml:mrow> </mml:math> to engineer the dispersion and reduce the insertion losses, allowing for tight mode confinement over extended lengths to achieve practical nonlinear conversion efficiencies ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m2"> <mml:mrow> <mml:mo form="prefix">∼</mml:mo> <mml:mtext>−</mml:mtext> <mml:mn>30</mml:mn> <mml:mtext> </mml:mtext> <mml:mi>dB</mml:mi> </mml:mrow> </mml:math> ) with modest pump powers ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m3"> <mml:mrow> <mml:mo form="prefix">∼</mml:mo> <mml:mn>48</mml:mn> <mml:mtext> </mml:mtext> <mml:mi>mW</mml:mi> </mml:mrow> </mml:math> ). Both amplitude and phase images are obtained using classically generated light, confirming the high degree of spatial and phase correlation of our system. The high powers ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m4"> <mml:mrow> <mml:mo form="prefix">&gt;</mml:mo> <mml:mn>10</mml:mn> <mml:mtext> </mml:mtext> <mml:mi>nW</mml:mi> </mml:mrow> </mml:math> ) and long coherence lengths ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m5"> <mml:mrow> <mml:mo form="prefix">&gt;</mml:mo> <mml:mn>4</mml:mn> <mml:mtext> </mml:mtext> <mml:mi>km</mml:mi> </mml:mrow> </mml:math> ) associated with our large fiber-based system result in high contrast and stable images.

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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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.873
Threshold uncertainty score0.741

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.191
GPT teacher head0.366
Teacher spread0.175 · 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