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Record W4408613888 · doi:10.1117/12.3046212

Reaching higher speed and sensitivity with single-THz-pulse and single-THz-photon detection

2025· article· en· W4408613888 on OpenAlexaff
Angela Gamouras, Nicolas Couture, Wei Cui, Markus Lippl, Défi Junior Jubgang Fandio, Aswin Vishnu Radhan, Eeswar Kumar Yalavarthi, Rachel Ostic, Nicolas Y. Joly, Jean‐Michel Ménard

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsNational Research Council CanadaMax Planck - University of Ottawa Centre for Extreme and Quantum PhotonicsUniversity of Ottawa
Fundersnot available
KeywordsTerahertz radiationSensitivity (control systems)OptoelectronicsPhotonOpticsPulse (music)Materials sciencePhysicsDetectorElectronic engineeringEngineering

Abstract

fetched live from OpenAlex

Real-time detection of terahertz (THz) radiation has the potential to unlock scientific discoveries in studies of fast and non-reproducible phenomena. Additionally, detection schemes able to resolve single THz photons are essential to enable a new range of quantum applications. Here, we demonstrate two distinct rapid and highly-sensitive approaches to the detection of pulsed THz radiation: i) a single-shot measurement technique which employs chirped-pulse spectral encoding and a dispersive Fourier transform method for time-resolved THz spectroscopy at rates up to 1.1 MHz; and ii) a single-THz-photon detection technique based on frequency upconversion and single-photon counting technology capable of zeptojoule THz detection. These innovative detection schemes pave the way for real-time monitoring of irreversible phenomena in organic materials, sensitive wireless signal detection, and highly non-degenerate quantum ghost imaging 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 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)0.001
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.010
GPT teacher head0.198
Teacher spread0.188 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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