Debiasing the Minimum-mass Extrasolar Nebula: On the Diversity of Solid Disk Profiles
Bibliographic record
Abstract
Abstract A foundational idea in the theory of in situ planet formation is the “minimum-mass extrasolar nebula” (MMEN), a surface density profile (Σ) of disk solids that is necessary to form the planets in their present locations. While most previous studies have fit a single power law to all exoplanets in an observed ensemble, it is unclear whether most exoplanetary systems form from a universal disk template. We use an advanced statistical model for the underlying architectures of multiplanet systems to reconstruct the MMEN. The simulated physical and Kepler-observed catalogs allow us to directly assess the role of detection biases, and in particular the effect of nontransiting or otherwise undetected planets, in altering the inferred MMEN. We find that fitting a power law of the form Σ = Σ 0 * ( a / a 0 ) β to each multiplanet system results in a broad distribution of disk profiles; Σ 0 * = 336 − 291 + 727 g cm−2 and β = − 1.98 − 1.52 + 1.55 encompass the 16th–84th percentiles of the marginal distributions in an underlying population, where Σ 0 * is the normalization at a 0 = 0.3 au. Around half of the inner planet-forming disks have minimum solid masses of ≳ 40M ⊕ within 1 au. While transit observations do not tend to bias the median β, they can lead to both significantly over- and underestimated Σ 0 * and thus broaden the inferred distribution of disk masses. Nevertheless, detection biases cannot account for the full variance in the observed disk profiles; there is no universal MMEN if all planets formed in situ. The great diversity of solid disk profiles suggests that a substantial fraction (≳23%) of planetary systems experienced a history of migration.
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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 | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".