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Record W4392017098 · doi:10.1364/ol.517916

Zeptojoule detection of terahertz pulses by parametric frequency upconversion

2024· article· en· W4392017098 on OpenAlexafffund
Défi Junior Jubgang Fandio, Aswin Vishnu Radhan, Eeswar Kumar Yalavarthi, Wei Cui, Nicolas Couture, Angela Gamouras, Jean‐Michel Ménard

Bibliographic record

VenueOptics Letters · 2024
Typearticle
Languageen
FieldEngineering
TopicTerahertz technology and applications
Canadian institutionsNational Research Council CanadaUniversity of Ottawa
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsPhoton upconversionTerahertz radiationOpticsTerahertz spectroscopy and technologyPhase matchingMaterials scienceParametric statisticsLaserOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

We combine parametric frequency upconversion with the single-photon counting technology to achieve terahertz (THz) detection sensitivity down to the zeptojoule (zJ) pulse energy level. Our detection scheme employs a near-infrared ultrafast source, a GaP nonlinear crystal, optical filters, and a single-photon avalanche diode. This configuration is able to resolve 1.4 zJ (1.4 × 10 –21 J) THz pulse energy, corresponding to 1.5 photons per pulse, when the signal is averaged within only 1 s (or 50,000 pulses). A single THz pulse can also be detected when its energy is above 1185 zJ. These numbers correspond to the noise-equivalent power and THz-to-NIR photon detection efficiency of 1.3 × 10 –16 W/Hz 1/2 and 5.8 × 10 –2 %, respectively. To test our scheme, we perform spectroscopy of the water vapor between 1 and 3.7 THz and obtain results that are in agreement with those acquired with a standard electro-optic sampling (EOS) method. Our technique provides a 0.2 THz spectral resolution offering a fast alternative to EOS THz detection for monitoring specific spectral components in spectroscopy, imaging, and communication applications.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.357

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 head0.003
GPT teacher head0.185
Teacher spread0.181 · 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 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

Citations21
Published2024
Admission routes2
Has abstractyes

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