METIS: The Imager - From design to verification
Bibliographic record
Abstract
METIS, the Mid-infrared ELT Imager and Spectrograph for the Extremely Large Telescope (ELT), is one of the three firstgeneration science instruments, it has passed final design [1],[2] and is midway the Manufacturing, Assembly, Integration and verification (MAIV) phase. The Imager will be completely assembled and tested at MPIA in Heidelberg, Germany, before integration into the METIS instrument in Leiden, Netherlands. The Imager sub-system provides diffraction-limited imaging capabilities and low-resolution grism spectroscopy in two channels: the first covers the atmospheric L&M bands with a field of view of 11x11 arcsec, the second covers the N band, with a field of view of 14x14 arcsec. The two channels are equipped with a HAWAII-2RG detector for LM band and a GeoSnap detector for the N band, respectively [3],[5]. Challenging requirements suitable for high contrast imaging require a thorough integration and verification, particularly considering the size, complexity and the operating temperature of the instrument. Virtually, all components and units are in production or are already finished. The integration and verification of these units follow an MAIV plan including a detailed alignment scheme. The AIV part consists of three different phases: acceptance tests of components, alignment tasks, and verification of requirements. A dedicated test cryostat has been developed for the end-to-end verification to guarantee a smooth integration into the METIS instrument. Here, we present this MAIV process and describe in detail exemplary individual tasks of each phase to demonstrate the complexity of this stage of development and the managing of the challenging procedures. We also describe the tools and the corresponding setups used in the laboratory to execute the various tests, and the application of interferometric measurements at cryogenic conditions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".