A Vector-Apodizing Phase Plate on the MICADO instrument on the ELT for Crowded Field observations - useful or not?
Bibliographic record
Abstract
We explore the implementation of a vector-Apodizing Phase Plate for crowded field observation on the European Extremely Large Telescope using the MICADO instrument. In this joint effort between Leiden Observatory and the Kapteyn Institute, we design an APP specifically suited for crowded fields in a two stepped approach: we first assume a circular symmetric aperture which allows us to perform cone optimization. This yields a global optimal APP design. Secondly, we use an implementation of Gerchberg-Saxton to optimize our design for the non-circular symmetric aperture of the ELT. We use METIS SCAO phase screens to generate a realistic PSF (with and without an APP) and convolve the resulting PSFs with two delta functions to create a toy model crowded field. We performed basic astrometric and photometric fitting by fitting two Airy disks on the resulting field, to map the quality of fit with and without an APP in place. We can conclude that implementing a vector-APP seems to improve the fitting quality, but better fitting techniques (such as PSF-modelling) need to be used to provide a definite conclusion.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".