Fast iterative algorithm (FIA) for controlling MEMS deformable mirrors: principle and laboratory demonstration
Bibliographic record
Abstract
We present a fast and high accuracy iterative algorithm to control Micro-Electro-Mechanical-System (MEMS) deformable mirrors (DMs) for open-loop (OL) adaptive optics (AO) applications. Our approach relies on a simple physical model for the forces applied on DM actuators and membrane, defined by a small number of parameters that we measure in an experimental setup. The algorithm iteratively applies forces and updates actuator displacements, allowing real-time utilization in an Extreme-AO system (control rate ≥ Khz). Our measurements show that it reproduces Kolmogorov type phase screens with an error equal to 7.3% of the rms of the desired phase (1.6% of the peak-to-valley of the desired phase). This performance corresponds to an improvement of a factor three compared to the standard quadratic model (common relation between voltage and actuator displacement). Originally developed for the DM control of the Subaru Coronagraphic Extreme-AO (SCExAO) project, the algorithm is also suitable for Multi-Object AO systems.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".