Mechanism of intensity noise suppression in Yb-doped phase-biased mode-locked NALM lasers
Bibliographic record
Abstract
Emerging applications in quantum spectroscopy, information processing, and time synchronization depend heavily on low-noise mode-locked lasers, as do microwave photonics and laser microscopy. Mode-locked fiber lasers have become a staple in this regard, most recently, the robust and self-starting phase-biased nonlinear amplifying loop mirror (NALM) lasers built with polarization-maintaining fiber. These lasers produce very low relative intensity noise (RIN), which has been further reduced by intracavity and extracavity filtering of amplified spontaneous emission (ASE) or via phase-bias variation, where in both cases the laser output's center wavelength was longer than the gain medium's peak emission wavelength. Here, for the first time for such lasers, we combine noise and power measurements with intracavity spectral filtering and measurement of pump-to-output transfer function. This novel approach reveals that filtering of pump noise via saturation of self-amplitude modulation (SAM) dominates RIN suppression across the board, where ASE plays a secondary role. We utilize this understanding to suppress RIN below the noise floor of our measurement system (-137 dBc/Hz over 100 Hz-1 MHz) by maximizing the saturation of SAM through adjustments of pump power and phase bias. Our findings make for a systematic and passive method for suppressing RIN.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".