Closed-Loop Until Further Notice: Comparing Predictive Control Methods in Closed-Loop
Bibliographic record
Abstract
For future extremely large telescopes, error in extreme adaptive optics systems at small angular separations willbe highly impacted by the lag time of the correction, which is typically on millisecond timescales; one solutionis to apply a predictive correction to catch up with the system delay. Predictive control leads to significantRMS error reductions in simulation (on the order of 5-10x improvement in RMS error compared with a standardintegral controller), but shows only modest improvement on-sky (less than 2x in RMS error). This performancelimitation is likely impacted by elements of pseudo open loop (POL) reconstruction, which requires assumptionsabout the response of the deformable mirror and accuracy of the wavefront measurements that are difficult toverify in practice. In this work, we explore a closed-loop method for data-driven prediction using a reformulatedempirical orthogonal functions (EOF). We examine the performance of the open and closed-loop methods insimulation on perfect systems and systems with an inaccurate understanding of the DM response.
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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 | 0.007 |
| 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 | 0.001 |
| 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".