A low-cost, high-speed, very high-order Shack-Hartmann sensor for testing TMT deformable mirrors
Bibliographic record
Abstract
The Thirty Meter Telescope will use a sophisticated adaptive optics system called NFIRAOS. This system utilizes two deformable mirrors conjugate to 0 km and 11.2 km to apply a Multi-Conjugate Adaptive Optics (MCAO) correction over a 2 arcminute field of view. DM0 and DM11 have 63 and 75 actuators across their respective diameters. To study the behavior of these mirrors, we have developed a low-cost, very high-order Shack-Hartmann Wavefront Sensor (WFS). We will use our WFS to calibrate the flatness of the DMs and measure the influence functions of the actuators. NFIRAOS is cooled to reduce the thermal emissivity of optical surfaces visible to the science detectors, so we will also measure the behaviour of the DMs in both warm and cold environments. As the cold chamber is prone to vibrations, a WFS is preferred to a phase-shifting interferometer. Our design was driven by the need to be able to evaluate the DM surface between the actuators, which led to the requirement of at least 248 sub apertures across the diameter. The largest commercially available Shack-Hartmann WFS has only 128 sub-apertures across the diameter, which is not enough to properly sample these DMs. Furthermore, the designed sensor is able to record the wavefront at 50 FPS (50 times per second) at full resolution. To fabricate this WFS, we used a commercial off-the-shelf CMOS detector, camera lens, and lens let array, which kept the total cost less than 20K USD. Here we present the design and performance characteristics of this device.
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 | 0.001 |
| 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".