Unlocking a lower shot noise limit in dual-comb interferometry
Bibliographic record
Abstract
Optimizing the signal-to-noise ratio (SNR) is critical to achieve high sensitivities across broad spectral ranges in dual-comb interferometry. Sensitivity can be improved through time-averaging, but only at the cost of reduced temporal resolution. We show that it is instead possible to use high-bandwidth detection combined with frequency-domain averaging of multiple copies of the dual-comb beat note. By controlling the signal and noise stationarity properties, one can even reduce the fundamental shot noise contribution compared to the normal, single copy, dual-comb operation where integration time is matched to, or larger than the repetition period. In principle, the use of Na aliased frequency-domain copies will improve SNR by up to Na, or equivalently, reduce acquisition time by a factor of Na. We demonstrate dual-comb interferometry using Na = 5 aliases, achieving the predicted fivefold reduction in shot noise power density at low frequencies. Over the full spectrum, unaveraged relative intensity noise limits the SNR, but we measure a 1.65× fold improvement in detection of CO2, corresponding to a 2.7× reduction in acquisition time for a given precision.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".