MétaCan
Menu
Back to cohort

Spectrally resolved wave-mixing between near- and far-infrared pulses in gas

2013· article· en· W2104458355 on OpenAlexafffund
Matteo Clerici, Daniele Faccio, Lucia Caspani, Marco Peccianti, Oded Yaakobi, Bruno E. Schmidt, Mostafa Shalaby, François Vidal, François Légaré, T. Ozaki, Roberto Morandotti

Bibliographic record

VenueNew Journal of Physics · 2013
Typearticle
Languageen
FieldEngineering
TopicTerahertz technology and applications
Canadian institutionsInstitut National de la Recherche Scientifique
FundersEngineering and Physical Sciences Research CouncilNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les TechnologiesEuropean CommissionMinistère du Développement Économique, de l’Innovation et de l’Exportation
KeywordsPhysicsTerahertz radiationMixing (physics)WavelengthElectric fieldAmplitudeOpticsInfraredCross-polarized wave generationFar infraredFour-wave mixingSpectrogramField (mathematics)Nonlinear opticsWave propagationLaser

Abstract

fetched live from OpenAlex

We investigate the nonlinear wave-mixing in gases between intense, short optical pulses and long-wavelength fields (mid infrared and terahertz). We show numerically that the beating between the sum-and difference-frequency generation components can be isolated in the spectrogram of the interaction, and can be used to sample the electric field oscillations of the long-wavelength pulses. This, in turn, could be employed as a possible characterization method that provides information on the real electric field amplitude. Our numerical model is supported by spectrally resolved measurements of the four-wave mixing signals obtained from the interaction of intense, single-cycle terahertz fields (lambda > 15 mu m) and optical pulses (lambda similar or equal to 800 nm, 50 fs duration) in air.

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 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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.919
Threshold uncertainty score0.304

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 head0.014
GPT teacher head0.219
Teacher spread0.205 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations14
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueNew Journal of PhysicsSame topicTerahertz technology and applicationsFrench-language works237,207