METIS first light imager and spectrograph for the ELT: overview of the near and mid-infrared detector subsystems
Bibliographic record
Abstract
The METIS instrument (Mid-infrared ELT Imager and Spectrograph) is one of the three first-light instruments for the ELT. It will work in the mid-infrared with a set of four different focal planes, grouped in three different subsystems: the imager (IMG) and the spectrograph (LMS) are the two scientific focal planes, and the last one, SCAO, is the dedicated adaptive optics system. In total, this instrument requires five H2RG detectors (5.3μm cutoff), one SAPHIRA detector (2.5μm) and one GEOSNAP (13.5μm). All of these detectors will be controlled by the New General Controller, second generation (NGCII). These three separate subsystems require specific tests and development : the IMG needs a fast readout for both N and LM channels, the LMS requires a mosaic of four detectors and SCAO works with one single detector operated fast for AO corrections. In this paper, we will present the challenges for the development of the detector systems of the three detector subunits in METIS. This includes the design, tests and preparations for the AIT/AIV phases that each subsystem has to go through. First, we describe the detector-specifics of all the instruments. In a second part, we go over the design challenges for these detector subunits. In the end, we will report on the current testing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".