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Record W2912922160 · doi:10.1364/optica.6.000160

Generating few-cycle radially polarized pulses

2019· article· en· W2912922160 on OpenAlexafffund
Fanqi Kong, Hugo Larocque, Ebrahim Karimi, P. B. Corkum, Chun-Mei Zhang

Bibliographic record

VenueOptica · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOrbital Angular Momentum in Optics
Canadian institutionsJoint Attosecond Science LaboratoryUniversity of Ottawa
FundersDefense Advanced Research Projects AgencyCanada Research ChairsCanada Foundation for InnovationOntario Research Foundation
KeywordsOpticsPhysicsHigh harmonic generationDiffractionBandwidth (computing)AttosecondBeam (structure)WavelengthGaussian beamLaserUltrashort pulseComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

A radially polarized beam is axially symmetric and is able to produce tightly focused light fields beyond the Gaussian beam diffraction limit. However, with the current technology, its duration is limited by the relatively narrow bandwidth that the generation techniques can support. Using a 10 cycle pulse with a central wavelength of 1.8 μm, we show that radially polarized beams can be compressed to the few-cycle regime, while still maintaining their radially polarized nature. Therefore, it seems feasible, using only well-developed methods, to reach focused intensities of ∼1019 W/cm2. Conversion via high-harmonic generation will also open a route for applications in attosecond science, especially for a wide range of optical measurements and optical control that require high spatial and high temporal resolution.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.004
GPT teacher head0.212
Teacher spread0.208 · 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

Citations45
Published2019
Admission routes2
Has abstractyes

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