Formation of planetary populations – I. Metallicity and envelope opacity effects
Bibliographic record
Abstract
We present a comprehensive body of simulations of the formation of exoplanetary populations that incorporate the role of planet traps in slowing planetary migration. The traps we include in our model are the water ice line, the disc heat transition, and the dead zone outer edge. We reduce our model parameter set to two physical parameters: the opacity of the accreting planetary atmospheres (κenv) and a measure of the efficiency of planetary accretion after gap opening (fmax). We perform planet population synthesis calculations based on the initial observed distributions of host star and disc properties – their disc masses, lifetimes, and stellar metallicities. We find the frequency of giant planet formation scales with disc metallicity, in agreement with the observed Jovian planet frequency–metallicity relation. We consider both X-ray and cosmic ray disc ionization models, whose differing ionization rates lead to different dead zone trap locations. In both cases, Jovian planets form in our model out to 2–3 au, with a distribution at smaller radii dependent on the disc ionization source and the setting of envelope opacity. We find that low values of κenv (0.001–0.002 cm2 g−1) and X-ray disc ionization are necessary to obtain a separation between hot Jupiters near 0.1 au, and warm Jupiters outside 0.6 au, a feature present in the data. Our model also produces a large number of super Earths, but the majority are outside of 2 au. As our model assumes a constant dust-to-gas ratio, we suggest that radial dust evolution must be taken into account to reproduce the observed super Earth population.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".