Development and characterization of a 2D precision cryogenic chopper for METIS
Bibliographic record
Abstract
The Mid-infrared E-ELT Imager and Spectrograph, or METIS, is foreseen as an early instrument for the European Extremely Large Telescope (E-ELT). A key part of METIS is the Cold Chopper (MCC) which switches the optical beam between the target and a nearby reference sky during observation for characterization of the fluctuating IR background signal in post-processing. This paper discusses the development and characterization of the realized MCC demonstrator. The chopper mirror (Ø64mm) should tip/tilt in 2D with a combined angle of up to 13.6mrad with 1.7μrad stability and repeatability within 5ms (95% duty cycle at 5Hz) at 80K. As these requirements cannot be met in the presence of friction or backlash, the mirror is guided by a monolithically integrated flexure mechanism. The angular position is actuated by three linear actuators and measured by three linear position sensors, resulting in a fast tip, tilt, and focus mirror. Using the third actuator introduces symmetry, and thus homogeneity in forces and heat flux. In an earlier paper, Ref. [1], the design of the chopper and the breadboard level testing of the key components were discussed. Since then, the chopper design has been revised to implement the lessons learned from the breadboard test and a demonstrator has been realized. This demonstrator has undergone an elaborate test program for characterization and performance validation in a cryogenic environment, as discussed in this paper.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".