Observation uncertainty effects on the precision of interior planetary parameters
Bibliographic record
Abstract
ABSTRACT Determining compositions of low-mass exoplanets is essential in understanding their origins. The certainty by which masses and radius are measured affects our ability to discern planets that are rocky or volatile rich. In this study, we aim to determine sound observational strategies to avoid diminishing returns. We quantify how uncertainties in mass, radius, and model assumptions propagate into errors in inferred compositions of rocky and water planets. For a target error in a planet’s iron-mass fraction or water content, we calculate the corresponding required accuracies in radius and mass. For instance, a rocky planet with a known radius error of 2 per cent (corresponding to TESS detection best errors) demands mass precision to be at 5–11 per cent to attain an 8 wt% precision in iron-mass fraction, regardless of mass. Similarly, a water world of equal radius precision requires 9–20 per cent mass precision to confine the water content within a 10 wt% margin. Lighter planets are more difficult to constrain, especially water-rich versus water-poor worlds. Studying Earth as an exoplanet, we find an ∼±5 point ’error floor’ in iron-mass fraction and ∼±7 in core-mass fraction from our lack of knowledge on mineralogy. The results presented here can quickly guide observing strategies to maximize insights into small exoplanet compositions while avoiding overobserving.
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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 | 0.019 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| 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".