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Record W2045764672 · doi:10.1364/oe.22.029152

Coherent dual-comb interferometry with quasi-integer-ratio repetition rates

2014· article· en· W2045764672 on OpenAlexafffund
Sylvain Boudreau, Jérôme Genest, Jean-Daniel Deschênes

Bibliographic record

VenueOptics Express · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsUniversité Laval
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsOpticsInterferometryPhysicsMultiplexingComb generatorFrequency combDeconvolutionImpulse responseImpulse (physics)Computer scienceMathematicsLaserTelecommunications

Abstract

fetched live from OpenAlex

We demonstrate a generalized method for dual-comb interferometry that involves the use of two frequency combs with quasi-integer-ratio repetition rates. We use a 16.67 MHz comb to probe an 80-cm-long ring cavity and a 100 MHz comb to asynchronously sample its impulse response. The resulting signal can be seen as six time-multiplexed independent interferograms. We perform a deconvolution of the photodetector's impulse response to prevent any crosstalk between these multiplexed data sets. The measurement is then demultiplexed and corrected with referencing signals. We obtain a measurement with a spectral point spacing of 16.67 MHz and a spectral SNR of 55 dB by averaging 15,000 interferograms, corresponding to a measurement time of 500 s. Compared to conventional dual-comb spectroscopy, this generalized technique allows to either reduce the spectral point spacing or the acquisition time by changing the repetition rate of only one of the combs.

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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.009
GPT teacher head0.243
Teacher spread0.234 · 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

Citations54
Published2014
Admission routes2
Has abstractyes

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