An iterative model for MEMS deformable mirrors
Bibliographic record
Abstract
We present a high accuracy Micro-Electro-Mechanical-System (MEMS) deformable mirror (DM) control algorithm currently implemented in the real-time control interface of the Subaru Coronagraphic Extreme Adaptive Optics project (SCExAO). MEMS DMs are an attractive DM technology for Extreme-AO (ExAO) because they offer unprecedented actuator density and actuator counts. ExAO applications require a DM model capable of reproducing a phase map with a precision of few nm rms. The algorithm relies (i) on a physical model of the actuators and the membrane and (ii) on the optimization of DM coefficients and geometrical coefficients, and could be adopted as an open-loop control solution for future MOAO or ExAO ELTs instruments. During the initial tests performed at the UVic AO Lab, the performance of the model reached an open-loop error equal to 7.3% of the rms of the desired phase (1.6% of the peak-to-valley (PV) of the desired phase) with Kolmogorov type wavefronts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".