Chemical fingerprints of formation in rocky super-Earths’ data
Bibliographic record
Abstract
ABSTRACT The composition of rocky exoplanets in the context of stars’ composition provides important constraints to formation theories. In this study, we select a sample of exoplanets with mass and radius measurements with an uncertainty $\lt 25{{\ \rm per\ cent}}$ and obtain their interior structure. We calculate compositional markers, ratios of iron to magnesium and silicon, as well as core mass fractions (CMFs) that fit the planetary parameters, and compare them to the stars. We find four key results that successful planet formation theories need to predict: (1) In a population sense, the composition of rocky planets spans a wider range than stars. The stars’ Fe/Si distribution is close to a Gaussian distribution $1.63^{+0.91}_{-0.85}$, while the planets’ distribution peaks at lower values and has a longer tail, $1.15^{+1.43}_{-0.76}$. It is easier to see the discrepancy in CMF space, where primordial stellar composition is $0.32^{+0.14}_{-0.12}$, while rocky planets follow a broader distribution $0.24^{+0.33}_{-0.18}$. (2) We introduce uncompressed density ($\overline{\rho _0}$ at reference pressure/temperature) as a metric to compare compositions. With this, we find what seems to be the maximum iron enrichment that rocky planets attain during formation ($\overline{\rho _0}\sim 6$ and CMF ∼0.8). (3) Highly irradiated planets exhibit a large range of compositions. If these planets are the result of atmospheric evaporation, iron enrichment and perhaps depletion must happen before gas dispersal. And, (4) We identify a group of highly irradiated planets that, if rocky, would be twofold depleted in Fe/Si with respect to the stars. Without a reliable theory for forming iron-depleted planets, these are interesting targets for follow-up.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".