Ultimate linewidth reduction of a semiconductor laser frequency-stabilized to a Fabry-Perot interferometer
Bibliographic record
Abstract
We report a theoretical dynamical analysis on effect of semiconductor laser phase noise on the achievable linewidth when locked to a Fabry-Pérot cavity fringe using a modulation-demodulation frequency stabilization technique such as the commonly used Pound-Drever-Hall frequency locking scheme. We show that, in the optical domain, the modulation-demodulation operation produces, in the presence of semiconductor laser phase noise, two kinds of excess noise, which could be much above the shot noise limit, namely, conversion noise (PM-to-AM) and intermodulation noise. We show that, in typical stabilization conditions, the ultimate semiconductor laser linewidth reduction can be severely limited by the intermodulation excess noise. The modulation-demodulation operation produces the undesirable nonlinear intermodulation effect through which the phase noise spectral components of the semiconductor laser, in the vicinity of even multiples of the modulation frequency, are downconverted into the bandpass of the frequency control loop. This adds a spurious signal, at the modulation frequency, to the error signal and limits the performance of the locked semiconductor laser. This effect, reported initially in the microwave domain using the quasistatic approximation, can be considerably reduced by a convenient choice of the modulation 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 | 0.000 | 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".