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Record W4308455294 · doi:10.1364/oe.470036

Solid-state biased coherent detection of ultra-broadband terahertz pulses generated in a spintronic emitter for high repetition rate, low pulse energy lasers

2022· article· en· W4308455294 on OpenAlexafffund
Tim Suter, Alessandro Tomasino, Matteo Savoini, Sarah Houver, Roberto Morandotti, Steven L. Johnson, Elsa Abreu

Bibliographic record

VenueOptics Express · 2022
Typearticle
Languageen
FieldEngineering
TopicTerahertz technology and applications
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaFreie Universität BerlinSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungMinistère de l'Économie, de la Science et de l'Innovation - Québec
KeywordsTerahertz radiationOpticsBroadbandLaserOptoelectronicsMaterials sciencePulse (music)Common emitterTerahertz spectroscopy and technologyTerahertz time-domain spectroscopyRepetition (rhetorical device)PhysicsDetector

Abstract

fetched live from OpenAlex

We report the coherent generation and detection of terahertz (THz) pulses featuring a spectral bandwidth in the range of 0.1-9 THz achieved via the use of a high repetition rate (250 kHz), low pulse energy (6.2 µJ) laser system. More specifically, we test and evaluate a solid-state biased coherent detection device in combination with a spintronic emitter. We demonstrate the use of this combination of techniques to measure the ultra-broadband THz frequency optical properties of bulk crystalline materials with time-domain spectroscopy.

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)0.001
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 score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.007
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

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

Citations4
Published2022
Admission routes2
Has abstractyes

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