Thirty Meter Telescope narrow-field infrared adaptive optics system real-time controller prototyping results
Bibliographic record
Abstract
Prototyping and benchmarking was performed for the Real-Time Controller (RTC) of the Narrow Field InfraRed Adaptive Optics System (NFIRAOS). To perform wavefront correction, NFIRAOS utilizes two deformable mirrors (DM) and one tip/tilt stage (TTS). The RTC receives wavefront information from six Laser Guide Star (LGS) Shack- Hartmann WaveFront Sensors (WFS), one high-order Natural Guide Star Pyramid WaveFront Sensor (PWFS) and multiple low-order instrument detectors. The RTC uses this information to determine the commands to send to the wavefront correctors. NFIRAOS is the first light AO system for the Thirty Meter Telescope (TMT). The prototyping was performed using dual-socket high performance Linux servers with the real-time (PREEMPT_RT) patch and demonstrated the viability of a commercial off-the-shelf (COTS) hardware approach to large scale AO reconstruction. In particular, a large custom matrix vector multiplication (MVM) was benchmarked which met the required latency requirements. In addition all major inter-machine communication was verified to be adequate using 10Gb and 40Gb Ethernet. The results of this prototyping has enabled a CPU-based NFIRAOS RTC design to proceed with confidence and that COTS hardware can be used to meet the demanding performance requirements.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".