Ultra-Dense CEO-Stabilized Broadband Optical Frequency Comb Generation Through Simple, Programmable, and Lossless 1000-Fold Frequency-Spacing Division
Bibliographic record
Abstract
Ultra-dense broadband optical frequency combs with sub-MHz frequency spacing and stabilized carrier-envelope offset (CEO) are needed for many important applications, including high-accuracy real-time sub-Doppler spectroscopy, precise characterization of photonic devices, and greenhouse gas sounding. However, the generation of such combs remains very challenging because they require mode-locked lasers with impractically long cavities (>few hundred meters). Here we demonstrate a CEO-stabilized optical frequency comb with a programmable sub-MHz frequency spacing, obtained through simple, suitable temporal phase modulation of a 250-MHz input comb. The method preserves the energy, the bandwidth, and the CEO-stabilization of the input comb, achieving a combined (CEO and repetition-rate) integrated phase noise below π/10, and a frequency spacing down to 250 kHz over a 5-dB bandwidth of 10 THz, i.e., corresponding to a record high 1000-fold frequency spacing reduction and more than 40 000 000 comb lines. The demonstrated method bridges the gap between presently available high-quality optical frequency combs and a host of demanding and important applications that require CEO-stabilized broadband frequency combs with sub-MHz spacings.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".