Development of a 2D precision cryogenic chopper for METIS
Bibliographic record
Abstract
The Mid-infrared E-ELT Imager and Spectrograph, or METIS, is foreseen as the third 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 elimination of the fluctuating IR background signal in post-processing. This paper discusses the development of the MCC demonstrator. The chopper mirror (Ø64mm) has to 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 to introduce symmetry, homogeneity in forces and heat flux is obtained. Both the actuators and the sensors are key components. A voice coil actuator had to be custom designed, to achieve the required acceleration force within the specified 1W heat load. The requirements for the displacement measurement can be met with a commercially available, fiber interferometry system. For integration of this system, stray light elimination is a critical design aspect and retro-reflectors have been used to reflect sufficient power into the fiber at large tip/tilt angles.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".