Control of dynamical regimes in optical microresonators exploiting parametric interaction
Bibliographic record
Abstract
Microresonators have the ability of strongly enhancing the propagating optical field, enabling nonlinear phenomena, such as bi-stability, self-pulsing and chaotic regimes, at very low powers. It is fundamental to comprehend the mechanisms that generate such dynamics, which are crucial for micro-cavities-based applications in communications, sensing and metrology. The aim of this work is to develop a scheme for the control of nonlinear regimes in microresonators, assuming the interplay between the ultra-fast Kerr effect and a slow intensity-dependent nonlinearity, such as thermo-optical effect. The framework of the coupled-mode theory is applied to model the system, while the bifurcation theory is used to investigate a configuration in which the power and frequency of a weak signal can control the behaviour of a strong pump. In this regards, this study demonstrates that the effect of a parametric interaction, specifically the four-wave mixing, plays a fundamental role in influencing the nature of the stationary states observed in a micro-cavity. The results show possible new strategies for enhanced, low-power, all-optical control of sensors, oscillators and chaos-controlled devices. Moreover, the outcomes provide new understanding of the effect of coherent wave mixing in the thermal stability regions of optical micro-cavities, including optical micro-combs.
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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 | 0.000 |
| 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".