Optimizing the subwavelength grating of
Bibliographic record
Abstract
Context. The annular groove phase mask (AGPM) is one possible implementation of the vector vortex coronagraph, where the helical phase ramp is produced by a concentric subwavelength grating. For several years, we have been manufacturing AGPMs by etching gratings into synthetic diamond substrates using inductively coupled plasma etching. Aims. We aim to design, fabricate, optimize, and evaluate new L-band AGPMs that reach the highest possible coronagraphic performance, for applications in current and forthcoming infrared high-contrast imagers. Methods. Rigorous coupled wave analysis (RCWA) is used for designing the subwavelength grating of the phase mask. Coronagraphic performance evaluation is performed on a dedicated optical test bench. The experimental results of the performance evaluation are then used to accurately determine the actual profile of the fabricated gratings, based on RCWA modeling. Results. The AGPM coronagraphic performance is very sensitive to small errors in etch depth and grating profile. Most of the fabricated components therefore show moderate performance in terms of starlight rejection (a few 100:1 in the best cases). Here we present new processes for re-etching the fabricated components in order to optimize the parameters of the grating and hence significantly increase their coronagraphic performance. Starlight rejection up to 1000:1 is demonstrated in a broadband L filter on the coronagraphic test bench, which corresponds to a raw contrast of about 10-5 at two resolution elements from the star for a perfect input wave front on a circular, unobstructed aperture. Conclusions. Thanks to their exquisite performance, our latest L-band AGPMs are good candidates for installation in state of the art and future high-contrast thermal infrared imagers, such as METIS for the E-ELT.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".