Robust supervised learning for closed loop adaptive optics predictive control
Bibliographic record
Abstract
Great progress has been made applying deep learning methods to adaptive optics (AO) control, focus has largely been on reinforcement learning (RL) methods. While RL is a powerful tool and shows promising results, it requires continual learning while on sky to truly be effective. This makes it difficult to apply optimization techniques, such as kernel compilation, pruning, or – in the most extreme cases – hard coded networks in hardware, which may be necessary for high speed extreme AO control. We present a method and optical bench results for supervised training of AO predictive control networks trained using only simulated data. This can be accomplished by varying both the optical parameters of the AO system as well as the parameters of the simulated atmosphere; teaching the network to generalize for optical as well as atmospheric conditions. Our method also alleviates issues with both online and supervised learning methods trained on saved telemetry which may over-fit to local conditions that can vary from night to night. This training methodology is general enough to be widely applicable among most AO systems and has proven to be effective in our optical bench experiments.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".