Real-Time State Discriminator for Searching Laser Cavity-Solitons in a Microresonator Filtered Fiber Laser
Bibliographic record
Abstract
Microcombs are optical frequency combs in microresonators [1], [2]. Laser cavity-soliton (LCS) states can be achieved in a system comprising a nonlinear Kerr microresonator nested in a fibre laser [3], resulting in a self-emergent, robust [4], and efficient [5] microcomb. Generally, the platform generally produces a broader variety of states depending on the specific parameter settings of the system. The two critical experimental parameters that need to be adjusted to achieve solitary oscillation via self-emergence [4] are the gain, which controls the state energy, and the length of the main cavity, which governs the group velocity mismatch between the two cavities. When searching for solitary oscillations, these parameters must be spanned over large ranges. While detailed information can be obtained through accurate but time-consuming methods like laser scanning spectroscopy and interferometry, real-time approaches are preferable when handling large datasets. In this work, we introduce a rapid method to distinguish typical lasing states, including soliton states, based on simple experimental properties. Specifically, our approach utilises radiofrequency (RF) data and spectral analysis to map states within nonlinear optical systems in real time, with minimal computational effort when handling large datasets.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".