Fast modulation and dithering for the NFIRAOS pyramid wavefront sensor
Bibliographic record
Abstract
The Narrow Field InfraRed Adaptive Optics System (NFIRAOS) for the Thirty Meter Telescope (TMT) will use a natural guide star (NGS) Pyramid Wavefront Sensor (PWFS). A 32-mm diameter Fast Steering Mirror (FSM) is used to modulate the position of the NGS image around the tip of the pyramid. The mirror traces out a circular tip/tilt pattern at up to 800 Hz (the maximum operating frequency of NFIRAOS), with a diameter chosen to balance sensitivity and dynamic range. A circular dither pattern at 1/4 the modulation frequency is superimposed to facilitate optical gain measurements. The timing of this motion is synchronized precisely with individual exposures on the PWFS detector, and must also be phased with other wavefront sensors, such as Laser Guide Star Wavefront Sensors (LGSWFS) and the On-Instrument Wavefront Sensors (OIWFS) of NFIRAOS client instruments (depending on the observing mode), to minimize latency. During trade studies it was decided to pursue a piezo actuator from Physik Instrumente (PI) using a monocrystalline piezo material, as more conventional polycrystalline devices would not meet the lifetime, stroke, and frequency requirements. Furthermore, PI claims excellent stability and hysteresis with similar piezo stages, rendering sensor feedback unnecessary. To characterize the performance of this mechanism, and to verify that it can function acceptably in open-loop, we have operated the stage on a test bench using a laser and high-speed position sensing devices (PSDs) both at room temperature and at the cold -30C operating temperature of NFIRAOS. We have also prototyped the software and hardware triggering strategy that will be used to synchronize the FSM with the rest of NFIRAOS.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".