The EChO science case
Bibliographic record
Abstract
The discovery of almost 2000 exoplanets has revealed an unexpectedly diverse planet population. Observations to date have shown that our Solar System is certainly not representative of the general population of planets in our Milky Way. The key science questions that urgently need addressing are therefore: What are exoplanets made of? Why are planets as they are? What causes the exceptional diversity observed as compared to the Solar System? EChO (Exoplanet Characterisation Observatory) has been designed as a dedicated survey mission for transit and eclipse spectroscopy capable of observing a large and diverse planet sample within its four-year mission lifetime. EChO can target the atmospheres of super-Earths, Neptune-like, and Jupiter-like planets, in the very hot to temperate zones (planet temperatures of 300K-3000K) of F to M-type host stars. Over the next ten years, several new ground- and space-based transit surveys will come on-line (e.g. NGTS, CHEOPS, TESS, PLATO), which will specifically focus on finding bright, nearby systems. The current rapid rate of discovery would allow the target list to be further optimised in the years prior to EChO's launch and enable the atmospheric characterisation of hundreds of planets. Placing the satellite at L2 provides a cold and stable thermal environment, as well as a large field of regard to allow efficient time-critical observation of targets randomly distributed over the sky. A 1m class telescope is sufficiently large to achieve the necessary spectro-photometric precision. The spectral coverage (0.5-11 micron, goal 16 micron) and SNR to be achieved by EChO, thanks to its high stability and dedicated design, would enable a very accurate measurement of the atmospheric composition and structure of hundreds of exoplanets.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.090 | 0.021 |
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".