Genetic algorithm-enhanced microcomb state generation
Bibliographic record
Abstract
Abstract Microcavities enable the generation of highly efficient microcombs, which find applications in various domains, such as high-precision metrology, sensing, and telecommunications. Such applications generally require precise control over the spectral features of the microcombs, such as free spectral range, spectral envelope, and bandwidth. Most existing methods for customizing microcomb still rely on manual exploration of a large parameter space, often lacking practicality and versatility. In this work, we propose a smart approach that employs genetic algorithms to autonomously optimize the parameters for generating and tailoring stable microcombs. Our scheme controls optical parametric oscillation in a microring resonator to achieve broadband microcombs spanning the entire telecommunication C-band. The high flexibility of our approach allows us to obtain complex microcomb spectral envelopes corresponding to various operation regimes, with the potential to be directly adapted to different microcavity geometries and materials. Our work provides a robust and effective solution for targeted soliton crystal and multi-soliton state generation, with future potential for next-generation telecommunication applications and artificial intelligence-assisted data processing.
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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 | 0.001 | 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".