MODELING THE FORMATION OF GIANT PLANET CORES. I. EVALUATING KEY PROCESSES
Bibliographic record
Abstract
One of the most challenging problems we face in our understanding of planet formation is how Jupiter and Saturn could have formed before the solar nebula dispersed. The most popular model of giant planet formation is the so-called core accretion model. In this model a large planetary embryo formed first, mainly by two-body accretion. This is then followed by a period of inflow of nebular gas directly onto the growing planet. The core accretion model has an Achilles heel, namely the very first step. We have undertaken the most comprehensive study of this process to date. In this study, we numerically integrate the orbits of a number of planetary embryos embedded in a swarm of planetesimals. In these experiments, we have included a large number of physical processes that might enhance accretion. In particular, we have included (1) aerodynamic gas drag, (2) collisional damping between planetesimals, (3) enhanced embryo cross sections due to their atmospheres, (4) planetesimal fragmentation, and (5) planetesimal-driven migration. We find that the gravitational interaction between the embryos and the planetesimals leads to the wholesale redistribution of material—regions are cleared of material and gaps open near the embryos. Indeed, in 90% of our simulations without fragmentation, the region near those embryos is cleared of planetesimals before much growth can occur. Thus, the widely used assumption that the surface density distribution of planetesimals is smooth can lead to misleading results. In the remaining 10% of our simulations, the embryos undergo a burst of outward migration that significantly increases growth. On timescales of ∼10 5 years, the outer embryo can migrate ∼6 AU and grow to roughly 30 M ⊕ . This represents a largely unexplored mode of core formation. We also find that the inclusion of planetesimal fragmentation tends to inhibit growth except for a narrow range of fragment migration rates.
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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) | 0.000 | 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 | 0.002 | 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".