Open-loop control demonstration of micro-electro-mechanical-system MEMS deformable mirror
Bibliographic record
Abstract
New astronomical challenges revolve around the observation of faint galaxies, nearby star-forming regions and the direct imaging of exoplanets. The technologies required to progress in these fields of research rely on the development of custom Adaptive Optics (AO) instruments such as Multi-Object AO (MOAO) or Extreme AO (ExAO). Many obstacles remain in the development of these new technologies. A major barrier to the implementation of MOAO is the utilisation of deformable mirrors (DMs) in an open-loop control system. Micro-Electro-Mechanical-System (MEMS) DMs show promise for application in both MOAO and ExAO. Despite recent encouraging laboratory results, it remains an immature technology which has yet to be demonstrated on a fully operational on-sky AO system. Much of the research in this area focuses on the development of an accurate model of the MEMS DMs. In this paper, a thorough characterization process of a MEMS DM is performed, with the goal of developing an open-loop control strategy free of computationally heavy modelling (such as the use of plate equations). Instead, a simpler approach, based on the additivity of the influence functions, is chosen. The actuator stroke-voltage relationship and the actuator influence functions are carefully calibrated. For 100 initial phase screens with a mean rms of 97 nm (computer generated following a Von Karman statistic), the resulting mean residual open-loop rms error is 16.5 nm, the mean fitting error rms is 13.3 nm and the mean DM error rms is 10.8 nm (error reflecting the performances of the model under test in this paper). This corresponds to 11% of residual DM error.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".