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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".