Heterodyne beats between a continuous-wave laser and a frequency comb beyond the shot-noise limit of a single comb mode
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".