Mode Visualization and Control of Complex Lasers Using Neural Networks
Why this work is in the frame
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Bibliographic record
Abstract
Visualizing the behavior of complex laser systems is an outstanding challenge, especially in the presence of nonlinear mode interactions. Hidden features, such as the gain distributions and spatial localization of lasing modes, often cannot be revealed experimentally, yet they are crucial to determining the laser action. Here, we introduce an experimental lasing spectroscopy method that visualizes the gain profiles of the modes in a complex, disorderly coupled microring array laser using an artificial neural network. The spatial gain distributions of the lasing modes are reconstructed without prior knowledge of the laser device. We further extend the neural network to a tandem neural network that can control the laser emission by matching the modal gain/loss profile to selectively enhance the targeted modes. This mode visualization method offers a new approach to extracting hidden spatial mode features from photonic structures, which could improve our understanding and control of complex photonic systems.
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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) | 0.000 | 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) | 0.000 | 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 it