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Record W4400724936 · doi:10.1117/12.3018975

Final design and status of the Mid-IR ELT Imager and Spectrograph, METIS

2024· article· en· W4400724936 on OpenAlexaboutno aff
Bernhard R. Brandl, Felix Bettonvil, R. van Boekel, Adrian M. Glauser, Sascha P. Quanz, Olivier Absil, M. Feldt, P. García, Alistair Glasse, M. Guêdel, Lucas Labadie, Michael Meyer, E. Pantin, Shiang‐Yu Wang, H. Van Winckel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCalibration and Measurement Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMetisSpectrographInfraredComputer sciencePhysicsOpticsAstronomyWorld Wide Web

Abstract

fetched live from OpenAlex

The Mid-Infrared ELT Imager and Spectrograph (METIS) will be one of only three 1st-generation science instruments on the 39m Extremely Large Telescope (ELT). METIS will provide diffraction-limited imaging and medium resolution slitspectroscopy from 3–13 microns (L, M, and N bands), as well as high resolution (R≈100,000) integral field spectroscopy from 2.9–5.3 microns. Both imaging and IFU spectroscopy can be combined with coronagraphic techniques. After the final design reviews of the optics (2021) and the entire system (2022), most hardware procurements have started. In this paper we present an overview of the status of the various ongoing activities. Many hardware components are already in hand, and the manufacturing is in full swing in order to start the assembly and testing of the subsystems in 2024 toward first light at the telescope in 2028/29. This rather brief paper only provides an overview of the project status. For more information, we refer to the detailed instrument paper which will be published soon.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.011
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0110.010

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.

Opus teacher head0.025
GPT teacher head0.233
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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".

Quick stats

Citations6
Published2024
Admission routes1
Has abstractyes

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