The ExoLife Finder (ELF) telescope project — a cutting-edge hybrid interferometer telescope explicitly designed for the high-contrast direct detection of exoplanets.
Bibliographic record
Abstract
The ExoLife Finder (ELF) is a groundbreaking ground-based facility that will transform exoplanet research through direct imaging and characterisation of terrestrial exoplanets. Our mission is to identify biomarkers within their atmospheres, and we are poised to achieve unparalleled performance levels by leveraging cutting-edge technologies. A significant advancement in our project is the development of advanced, ultra-light, and ultra-thin self-correcting mirrors crafted using state-of-the-art 3D printing technology with electro-active actuators. We are confident that these innovations will significantly enhance our ability to uncover the secrets of distant worlds. The ELF consortium, headquartered at the Instituto de Astrofísica de Canarias (IAC) in Tenerife, is constructing a 3.5-meter SELF (Small-ELF) prototype. This technology demonstrator will pave the way for the larger 25-meter ELF telescope. SELF will comprise 15 off-axis active "live" mirrors, each with a diameter of 0.5 meters, arranged in a tensegrity structure. This configuration will function as a fixed pupil interferometer, engineered for high-contrast imaging through direct “dark hole” coronagraphy. To meet the rigorous demands of this system, we are also implementing specialised extreme adaptive optics (XAO) systems to effectively manage the diluted apertures and ensure the highest contrast results. The conclusion of the SELF project is scheduled to take place at IAC's Teide Observatory in the coming years. The goal is to showcase how technological innovations can enhance performance in cost-effective, larger telescopes, especially in the search for extraterrestrial life within a few parsecs of the Sun. This presentation will outline the telescope's design, its specialised high-contrast imaging capabilities — including new developments in wavefront sensing — and the groundbreaking advancements in producing self-correcting "live" mirrors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".