Exoptf Science Uniquely Enabled By Far-ir Interferometry: Probing The Formation Of Planetary Systems, And Finding And Characterizing Exoplanets
Bibliographic record
Abstract
By providing sensitive sub-arcsecond images and integral field spectroscopy in the 25 – 400 ìm wavelength range, a far-IR interferometer will revolutionize our understanding of planetary system formation, reveal otherwise-undetectable planets through the disk perturbations they induce, and spectroscopically probe the atmospheres of extrasolar giant planets in orbits typical of most of the planets in our solar system. The technical challenges associated with interferometry in the far-IR are greatly relaxed relative to those encountered at shorter wavelengths or when starlight nulling is required. A structurally connected far-IR interferometer with a maximum baseline length of 36 m can resolve the interesting spatial structures in nascent and developed exoplanetary systems and measure exozodiacal emission at a sensitivity level critical to TPF-I mission planning. The Space Infrared Interferometric Telescope was recommended in the Community Plan for Far-IR/Submillimeter Space Astronomy, studied as a Probe-class mission, and estimated to cost $800M. The scientific communities in Europe, Japan, and Canada have also demonstrated a keen interest in far-IR interferometry through mission planning workshops and technology research, suggesting the possibility of an international collaborative effort.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".