Linear dual-comb interferometry at high power levels
Bibliographic record
Abstract
Detector non-linearity is an important factor limiting the maximal power and hence the signal-to-noise ratio (SNR) in dual-comb interferometry. To increase the SNR without overwhelming averaging time, photodetector non-linearity must be properly handled for high input power. Detectors exhibiting nonlinear behavior can produce linear dual-comb interferograms if the area of the detector’s impulse response does not saturate and if the overlap between successive time-varying impulse responses is properly managed. Here, a high bandwidth non-amplified balanced photodetector is characterized in terms of its impulse response to high intensity short pulses to exemplify the conditions. With a 23.5 mW average power on each detector in a balanced pair, nonlinear spectral artifacts are at least 40 dB below the spectral baseline. Absorption lines of carbon dioxide are measured to reveal lines discrepancies smaller than 0.1% with HITRAN. A spectral shape independent formulation for the dual-comb figure of merit is proposed, reaching here 7.2 × 10 7 Hz 1/2 limited by laser relative intensity noise, but corresponding to an ideal, shot-noise limited, figure of merit for an equivalent 0.85 mW average power per comb.
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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 | 0.000 | 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) | 0.002 | 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".