End-to-end performance simulations of infrared vortex coronagraphs for extremely large telescopes.
Bibliographic record
Abstract
Since the first discovery of an exoplanet in the 1990s, the number of confirmed exoplanets has increased exponentially, with several ground-and space-based missions dedicated to exoplanet detection. Most of these detections have been obtained via indirect methods, in which the presence of the exoplanets is derived by its effect on the host star. Direct detection methods, in which the exoplanet is observed directly, are still in the background, but the improvement of hardware and software techniques is progressively closing the gap with the most successful detection methods. The Extremely Large Telescope (ELT) is one of the most anticipated telescopes of the next years. With its 39m diameter, it will be the biggest eye on the universe in the optical/infrared range. It will be equipped with three state-of-the-art instruments: HARMONI (the high angular optical and near-infrared spectrograph), MICADO (the imaging camera for deep observations) working in tandem with MORFEO (the multiconjugate adaptive optics system), and METIS (the mid-infrared imager and spectrograph). One of the main science cases for the latter is direct detection of exoplanets, and, for this purpose, it will be equipped with two of the most advanced coronagraphs, the apodized phase plate (APP) and the vortex coronagraph (VC). In this dissertation, we present the results of our work on the preparation of the high-contrast imaging modes of METIS. The coronagraphs are optimized for the instrument wavebands, and end-to-end performance simulations are performed for several instrumental and environmental parameters. The expected observational capabilities of the METIS instrument show the great leap in sensitivity to faint companions in the thermal infrared regime that the instrument will enable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".