Bibliographic record
Abstract
The relative phase between two fiber lasers is controlled via a high performance optical phase-locked loop (OPLL). Two parameters are of particular importance for the design: the intrinsic phase noise of the laser (i.e. its linewidth) and a high-gain, low-noise electronic locking loop. In this work, one of the lowest phase noise fiber lasers commercially available was selected (i.e. NP Photonics Rock fiber laser module), with sub-kHz linewidth at 1550.12 nm. However, the fast tuning mechanism of such lasers is through stretching its cavity length with a piezoelectric transducer which has a few 10s kHz bandwidth. To further increase the locking loop bandwidth to several MHz, a second tuning mechanism is used by adding a Lithium Niobate phase modulator in the laser signal path. The OPLL is thus divided into two locking loops, a slow loop acting on the laser piezoelectric transducer and a fast loop acting on the phase modulator. The beat signal between the two phase-locked lasers yields a highly pure sine wave with an integrated phase error of 0.0012 rad. This is orders of magnitude lower than similar existing systems such as the Laser Synthesizer used for distribution of photonic local oscillator (LO) for the Atacama Large Millimeter Array radio telescope in Chile. Other applications for ultra-low noise OPLL include coherent power combining, Brillouin sensing, light detection and ranging (LIDAR), fiber optic gyroscopes, phased array antenna and beam steering, generation of LOs for next generation coherent communication systems, coherent analog optical links, terahertz generation and coherent spectroscopy.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".