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Record W3111714208 · doi:10.1117/12.2562219

Warm calibration unit of the mid-infrared E-ELT instrument METIS: overview and current status of the project

2020· article· en· W3111714208 on OpenAlexaboutno aff
Monika Rutowska, T. K. Sharma, Michael Wiest, Sabine Graf, C. Straubmeier, Steffen Rost, Lucas Labadie, A. Eckart, L. Burtscher, Tibor Agócs, Dirk Lesman, R. Stuik, Adrian M. Glauser, Bernhard R. Brandl, Felix Bettonvil

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsnot available
FundersBundesministerium für Bildung und Forschung
KeywordsMetisSpectrographCryostatCalibrationEngineeringComputer sciencePhysicsAstronomy

Abstract

fetched live from OpenAlex

The warm calibration unit (WCU) is one subsystem of the future METIS instrument on the European Extremely Large Telescope (E-ELT). Operating at daytime temperature, the WCU is mounted above the main cryostat of METIS and will be employed as calibration reference for science observations, as well as for verification and alignment purposes during the AIT phase. The WCU is designed and constructed at the University of Cologne, partner in the METIS consortium. The WCU, together with the full METIS instrument, went recently through a successful preliminary design review (PDR) phase at ESO and is entering now the Phase C of the project. In this paper, we present the current status of the WCU and summarize the mostly mechanical and optical engineering work. We adopted a hexapod unit to interface with the METIS cryostat and a CFRP-based optical bench to optimally cope with alignment flexure. We develop the case for fiber-fed laser sources feeding the integrating sphere for spectral calibration of the LM-Spectrograph of METIS. We detail the activity foreseen for Phase C including the optical tolerances analysis, the eigenfrequency and earthquake analysis and a preparation of the sub-system MAIT work, finishing the paper with a short overview of the WCU future plans.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.469
Threshold uncertainty score0.179

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.060
GPT teacher head0.282
Teacher spread0.222 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations1
Published2020
Admission routes1
Has abstractyes

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