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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".