Can Large-scale Migration Explain the Giant Planet Occurrence Rate?
Bibliographic record
Abstract
Abstract The giant planet occurrence rate rises with orbital period out to at least ∼300 days. Large-scale planetary migration through the disk has long been suspected to be the origin of this feature, as the timescale of standard Type I migration in a standard solar nebula is longer farther from the star. These calculations also find that typical Jupiter-bearing cores shuttle toward the disk inner edge on timescales orders of magnitude shorter than the gas disk lifetime. The presence of gas giants at myriad distances requires mechanisms to slow large-scale migration. We revisit the migration paradigm by building model occurrence rates to compare to the observations, computing simultaneously the migration of cores, their mass growth by gas accretion, and their gap opening. We show explicitly that the former two processes occur in tandem. Radial transport of planets can slow down significantly once deep gaps are carved out by their interaction with disk gas. Disks are more easily perturbed closer to the star, so accounting for gap opening flattens the final orbital period distribution. To recover the observed rise in occurrence rate, gas giants need to be more massive farther out, which is naturally achieved if their envelopes are dust-free. We find that only a narrow region of parameter space can recover the observed giant planet occurrence rate in orbital period, but not simultaneously the mass distribution of low-eccentricity giant planets. This challenges disk migration as the dominant origin channel of hot and warm Jupiters. Future efforts in characterizing the unbiased mass distribution will place stronger constraints on predictions from migration theory.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".