Design, manufacturing and prototype testing of the METIS ring apodizer
Bibliographic record
Abstract
The Mid-Infrared ELT Imager and Spectrograph (METIS) is one of the three first-generation instruments of the Extremely Large Telescope (ELT). METIS will enable high-contrast imaging through different coronagraphic modes, one of which is implemented with a vortex coronagraph. However, the complex pupil of the ELT with its large central obscuration limits the performance of a classical vortex coronagraph. Using a grayscale ring apodizer in a pupil plane upstream of the vortex phase mask can correct for the effect of the central obscuration and partly restore the coronagraphic performance of the vortex for the ELT pupil. The coronagraphic performance of the ring-apodized vortex coronagraph relies on the precise control of transmission in the grayscale region, which can be implemented using a technology based on chromium microdots. Here, we present the ring apodizer for the METIS instrument exploiting the microdots technology. We first describe the design process of microdot patterns and perform simulations for both transmission and phase response of the microdots with various geometric parameters. We then describe the manufacturing of three prototypes featuring different design parameters, as well as their testing in terms of transmission and phase response. Finally, capitalizing on the lessons learned, we report on the current status of manufacturing and testing of the final ring apodizer for METIS.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".