The game-changing promises of ELT/METIS for exoplanet imaging
Bibliographic record
Abstract
METIS, the mid-infrared ELT imager and spectrograph, is one of the three first-generation instruments of the Extremely Large Telescope. It recently passed its preliminary design review, and is expected to see first light in 2027. Specifically designed to deliver high-contrast imaging capabilities, METIS features advanced coronagraphic concepts such as a ring-apodized vortex coronagraph and an apodizing phase plate. It also comprises a high-resolution (R=100,000) integral field spectrograph covering wavelengths from 3 to 5 µm, which can be combined with the coronagraphic observing modes. Using the full resolving power of the ELT thanks to a high-performance adaptive optics module, METIS promises to reach game-changing performance in the field of exoplanet imaging. In this talk, I will review the design and expected performance of METIS high-contrast imaging modes. I will then describe some of the most appealing science cases that will be addressed by METIS in the field of exoplanet imaging, including the possible discovery of temperate rocky planets in the alpha Centauri system, the high-spectral resolution study of Proxima b, or the spectral characterization of exoplanets discovered by radial velocities or astrometry (Gaia follow-up). I will conclude this talk by decribing our plans to deploy machine learning techniques in the operation and data analysis of the METIS high contrast imaging modes.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".