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Performance analysis of tabletop single-pulse terahertz detection at rates up to 1.1 MHz

2024· article· en· W4396760600 on OpenAlexafffund
Nicolas Couture, Markus Lippl, Wei Cui, Angela Gamouras, Nicolas Y. Joly, Jean‐Michel Ménard

Bibliographic record

VenuePhysical Review Applied · 2024
Typearticle
Languageen
FieldEngineering
TopicTerahertz technology and applications
Canadian institutionsNational Research Council CanadaMax Planck - University of Ottawa Centre for Extreme and Quantum PhotonicsUniversity of Ottawa
FundersMax-Planck-GesellschaftNatural Sciences and Engineering Research Council of CanadaFraunhofer-GesellschaftBundesministerium für Bildung und ForschungNational Research Council CanadaCanada Foundation for Innovation
KeywordsTerahertz radiationPulse (music)Pulse rateOpticsPhysicsOptoelectronicsDetectorMedicine

Abstract

fetched live from OpenAlex

Standard terahertz time-domain spectroscopy uses a relatively slow multidata acquisition process that has hindered the technique's ability to resolve ``fast'' dynamics occurring on the microsecond timescale. This timescale, inaccessible to most ultrafast pump-probe techniques, hosts a range of phenomena that has been left unexplored due to a lack of proper real-time monitoring techniques. In this work, chirped-pulse spectral encoding, a photonic time-stretch technique, and high-speed electronics are used to demonstrate time-resolved terahertz detection at a rate up to 1.1 MHz. This configuration relies on a tabletop optical source and a setup able to resolve every terahertz transient generated by the same source. We investigate the performance of this single-pulse terahertz detection system at different acquisition rates in terms of experimental noise, dynamic range, and signal-to-noise ratio. Our results pave the way towards single-pulse terahertz time-domain spectroscopy at arbitrarily fast rates to monitor complex dynamics in real time.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.012
GPT teacher head0.265
Teacher spread0.253 · 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

Citations6
Published2024
Admission routes2
Has abstractyes

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