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Record W2334695088 · doi:10.1103/physreva.87.023802

Heterodyne beats between a continuous-wave laser and a frequency comb beyond the shot-noise limit of a single comb mode

2013· article· en· W2334695088 on OpenAlexafffund
Jean-Daniel Deschênes, Jérôme Genest

Bibliographic record

VenuePhysical Review A · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsOpticsHeterodyne (poetry)Heterodyne detectionContinuous waveFrequency combLaserBeat (acoustics)Shot noiseNoise (video)AcousticsComputer science

Abstract

fetched live from OpenAlex

We demonstrate a simple and robust technique for improving the signal-to-noise ratio (SNR) of heterodyne beats between a low-power frequency comb and a continuous-wave (cw) laser. By exploiting the pulsed nature of the comb, a SNR better than the shot-noise limit for the beating of a single mode can be obtained. This technique relies on electrical gating of the beat signal to remove as much shot noise as possible from the measurement. This technique can also be understood as efficiently using the beating of the cw laser with many comb modes simultaneously. We demonstrate a simple experimental setup which achieves a 50-fold improvement in the shot-noise limited SNR of a conventional heterodyne setup and is in long-term agreement with a conventional heterodyne setup, at least to better than 1 part in ${10}^{17}$. The resulting analog signal is completely compatible with existing heterodyne setups and can be used as an upgrade to an existing setup which would benefit from the SNR improvement.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.773
Threshold uncertainty score0.689

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.027
GPT teacher head0.282
Teacher spread0.255 · 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 designTheoretical or conceptual
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

Citations30
Published2013
Admission routes2
Has abstractyes

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