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Record W3129597232 · doi:10.3390/photonics8030064

Synthesis of Super-Oscillatory Point-Spread Functions with Taylor-Like Tapered Sidelobes for Advanced Optical Super-Resolution Imaging

2021· article· en· W3129597232 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 · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOrbital Angular Momentum in Optics
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTaperingGratingOpticsChebyshev filterPoint (geometry)Taylor seriesPhysicsComputer scienceMathematicsMathematical analysisGeometry

Abstract

fetched live from OpenAlex

Recently, the super-oscillation phenomenon has attracted attention because of its ability to super-resolve unlabelled objects in the far-field. Previous synthesis of super-oscillatory point-spread functions used the Chebyshev patterns where all sidelobes are equal. In this work, an approach is introduced to generate super-oscillatory Taylor-like point-spread functions that have tapered sidelobes. The proposed method is based on the Schelkunoff’s super-directive antenna theory. This approach enables the super-resolution, the first sidelobe level and the tapering rate of the sidelobes to be controlled. Finally, we present the design of several imaging experiments using a spatial light modulator as an advanced programmable grating to form the Taylor-like super-oscillatory point-spread functions and demonstrate their superiority over the Chebyshev ones in resolving the objects of two apertures and of a mask with the letter E.

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.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score0.838

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.220
Teacher spread0.213 · 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