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Record W1999581419 · doi:10.1364/oe.23.009295

Chirped pulse heterodyne for optimal beat note detection between a frequency comb and a continuous wave laser

2015· article· en· W1999581419 on OpenAlexafffund
Jean-Daniel Deschênes, Jérôme Genest

Bibliographic record

VenueOptics Express · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBeat (acoustics)OpticsHeterodyne detectionBandwidth (computing)LaserContinuous waveFrequency combPhysicsComb filterUltrashort pulseMaterials scienceTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

Chirped pulse heterodyne is proposed to maximize the signal-to-noise ratio (SNR) when measuring the beat note between an optical frequency comb and a continuous wave (CW) laser. The noise model reveals that all the comb power within the largest possible detection bandwidth can be used to increase the SNR. The chirped comb/CW interference experiment is shown to be equivalent to CW/CW interference, using the comb's spectrally available power. The approach can also greatly alleviate dynamic range issues when detected pulsed heterodyne signals. A beat note SNR of 68.3 dB in a 100 kHz bandwidth is achieved.

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 score0.428
Threshold uncertainty score0.838

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 head0.023
GPT teacher head0.262
Teacher spread0.239 · 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

Citations9
Published2015
Admission routes2
Has abstractyes

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