METIS AOCS at the beginning of MAIT
Bibliographic record
Abstract
The Mid-infrared ELT Imager and Spectrograph (METIS) instrument is one of three first-generation science instruments for the Extremely Large Telescope (ELT) in Chile. It has entered the Manufacturing, Assembly, Integration and Testing (MAIT) phase and it is currently scheduled to be installed in 2028. Its Single Conjugate Adaptive Optics (SCAO) system will provide the performance of an extreme adaptive optics system which enables High-Contrast Imaging (HCI) observations in the thermal/mid-infrared wavelength domain. The METIS Adaptive Optics (AO) control system is responsible for the AO wavefront correction and for supporting AO-related assembly, integration, verification and maintenance activities. It realizes the main AO loop by a Real-Time Computer (RTC) that receives images from a wavefront sensor and commands the corrective optics through the Central Control System (CCS) of the ELT. Several auxiliary functions will run outside of the RTC in the AO Observation Coordination System (AO OCS) that are necessary to maintain the quality of the wavefront correction. For instance, the Differential tip-tilt (DTT) control loop centers the star on the Vortex Phase Mask during HCI observations by adjusting the modulator device via the SCAO Function Control System (FCS) based on sciences images received from the Focal Plane Sensor Gateway (FPS GW). Conceptually, the METIS Adaptive Optics Control System (AOCS) is a distributed software system that is controlled by the METIS Instrument Control System (ICS). This paper describes the current status of the METIS AO control system, driving forces behind the design and the important control loops.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.048 | 0.023 |
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".