The High-contrast End-to-End Performance Simulator (HEEPS): influence of ELT/METIS instrumental effects
Bibliographic record
Abstract
The High-contrast End-to-End Performance Simulator (HEEPS) is an open-source python-based software with a modular and extensible architecture, that creates end-to-end simulations of high contrast imaging (HCI) instruments. It uses the wavefront Fresnel propagation package PROPER, the telescope instrument data simulator ScopeSim, and the HCI image processing package VIP. In this paper, we present the design of HEEPS, and motivate its baseline structure with the implementation of the Mid-infrared ELT Imager and Spectrograph (METIS) HCI modes, including coronagraphic components such as vortex phase masks, ring apodizers, and apodizing phase plates. Then, we present the key results of our thorough end-to-end simulations starting from 1-hour AO residual phase screens produced with the end-to-end AO simulator COMPASS. We analyze various undesirable effects such as pupil effects (stability, uniformity, drift) and noncommon path phase and amplitude errors. Finally, the coronagraphic performance including all effects is shown for all the METIS HCI modes as 5-sigma sensitivity contrast curves after ADI post-processing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".