Computer-controlled harmonic FM mode-locking of 40-GHz repetition-rate fiber laser
Bibliographic record
Abstract
Active FM harmonic mode-locking of a fiber laser by intracavity phase modulation allows obtaining of stable laser pulses with a high repetition rate. Driving the phase modulator by an external RF synthesizer has an advantage of constantly applying only one modulation frequency to the phase modulator. That greatly facilitates the generation of stable laser pulses with very small noise. However, this approach requires constant frequency tuning of the synthesizer to compensate for small temperature fluctuations causing changes in the fundamental frequency of the laser cavity. In order to control the modulation frequency we mixed the RF signal from the laser output detected by a fast photodiode with the signal from the synthesizer. The amplitude of the measured DC component of the mixed signal depends on the phase difference of the two signals. The phase difference varies approximately linear with the laser detuning near the mode locking resonance. We develop software that performs constant measurement of the mixed signal and tuning the modulation frequency in order to keep the DC component of the mixed signal at a preset value. The program performed approximately two auto-tuning steps per second. The presented method allows very simple and reliable obtaining of stable computer controlled harmonic mode-locking of a fiber laser at 40 GHz repetition rate frequency.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".