Responsivity and stability of nonlinear exceptional point lasers with saturable gain and loss
Bibliographic record
Abstract
The responsivity of perturbation sensing can be effectively enhanced by using higher-order exceptional points (EPs) due to their nonlinear response to frequency perturbations. However, experimental realization can be difficult due to the stringent parameter conditions associated with these points. In this work we study an EP laser composed of two coupled nonlinear resonators that uses nonlinearity to simplify these tuning requirements. This system demonstrates a distinct cube-root response in the steady-state lasing frequency, with a constant of proportionality that depends on the distribution of linear and saturable gain and loss. This design freedom enables several orders of magnitude higher responsivity than systems with a single nonlinear resonator, which have been previously explored. Maximizing responsivity also improves the robustness of sensing performance against parametric errors. These features are derived from coupled-mode theory and further supported by steady-state ab initio laser theory results at several nonlinear EPs. Through linear stability analysis, we also identify regions of instability within the class-A regime that arise due to mode competition, which can be induced by asymmetric passive losses. In the class-B regime, we show that the interplay between gain dynamics and detuning can lead to restabilization at slow relaxation rates or higher interresonator coupling rates. This regime could be used to increase the maximum achievable responsivity of the system.
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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 | 0.001 |
| 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 | 0.000 | 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 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".