Maximizing Ariel's Survey Leverage for Population-Level Studies of Exoplanets
Bibliographic record
Abstract
ESA's Ariel mission will be uniquely suited to performing population-level studies of exoplanets. Most of these studies consist of quantifying trends between an Ariel-measured quantity, y, and an a priori planetary property, x; for example, atmospheric metallicity as inferred from Ariel transit spectroscopy vs. planetary mass. The precision with which we can quantify such trends depends on the number of targets in the survey and their variance in the a priori parameter. We define the leverage of a survey with N targets as L = sqrt(N)stdev(x) and show that it quantitatively predicts the precision of population-level trends. The target selection challenge of Ariel can therefore be summarized as maximizing L along some axes of diversity for a given cumulative observing time. To this end, we consider different schemes to select the mission reference sample for a notional three year transit spectroscopy survey with Ariel. We divide the exoplanets in the mission candidate sample into logarithmic classes based on radius, equilibrium temperature and host star temperature. We then construct a target list by cyclically choosing the easiest remaining target in each class. We find that the leverage on a single axis of diversity can be increased by dividing that axis into many classes, but this sacrifices leverage along other axes of diversity. We conclude that a modest number of classes, possibly only one, should be defined when selecting Ariel targets. Lastly, we note that the statistical leverage of the Ariel transit survey would be significantly increased if current candidate planets were confirmed. This highlights the urgency of vetting and confirming the easiest transmission and emission spectroscopy targets in the Ariel mission candidate sample.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".