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Record W2583808447 · doi:10.3390/photonics4010007

Phase-Insensitive Scattering of Terahertz Radiation

2017· article· en· W2583808447 on OpenAlexafffund
Mihail Petev, Niclas Westerberg, E. Rubino, Daniel Moss, A. Couairon, François Légaré, Roberto Morandotti, Daniele Faccio, Matteo Clerici

Bibliographic record

VenuePhotonics · 2017
Typearticle
Languageen
FieldEngineering
TopicTerahertz technology and applications
Canadian institutionsInstitut National de la Recherche Scientifique
FundersEngineering and Physical Sciences Research CouncilEuropean Research CouncilNatural Sciences and Engineering Research Council of CanadaSeventh Framework ProgrammeFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsTerahertz radiationScatteringOpticsPhysicsMixing (physics)Electric fieldNonlinear opticsNonlinear systemBroadbandRadiationFour-wave mixingTerahertz gapTerahertz spectroscopy and technologyOptoelectronicsMaterials scienceTerahertz metamaterialsFar-infrared laserLaser

Abstract

fetched live from OpenAlex

The nonlinear interaction between Near-Infrared (NIR) and Terahertz pulses is principally investigated as a means for the detection of radiation in the hardly accessible THz spectral region. Most studies have targeted second-order nonlinear processes, given their higher efficiencies, and only a limited number have addressed third-order nonlinear interactions, mainly investigating four-wave mixing in air for broadband THz detection. We have studied the nonlinear interaction between THz and NIR pulses in solid-state media (specifically diamond), and we show how the former can be frequency-shifted up to UV frequencies by the scattering from the nonlinear polarisation induced by the latter. Such UV emission differs from the well-known electric field-induced second harmonic (EFISH) one, as it is generated via a phase-insensitive scattering, rather than a sum- or difference-frequency four-wave-mixing process.

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 score0.001
Threshold uncertainty score0.004

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.274
Teacher spread0.261 · 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

Citations6
Published2017
Admission routes2
Has abstractyes

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