Saving Planetary Systems: Dead Zones and Planetary Migration
Bibliographic record
Abstract
The tidal interaction between a disk and a planet leading to a planet's migration is widely believed to be the mechanism that explains the variety of orbital radii of extrasolar planets. A long-standing question is what stops the migration before planets plunge into their central stars. We propose a new, simple mechanism to significantly slow down planet migration and test it using a hybrid numerical integrator to simulate disk-planet interaction. Key to this scenario are the low-viscosity regions in protostellar disks known as dead zones. Low viscosity affects planetary migration in two ways. First, it allows a smaller mass planet to open a gap, and hence trade the faster type I migration (pre-gap-opening migration) for the slower type II migration (post-gap-opening migration). Second, low viscosity slows down type II migration itself, because type II migration varies directly with viscosity. We present numerical simulations of planetary migration in disks using a hybrid symplectic integrator-gas dynamics code. Assuming that the disk viscosity parameter inside the dead zone is a = 10 to 10, we find that, when a low-mass planet (1-10 M?) migrates from outside the dead zone, it is stopped by mass accumulation inside the dead zone. When a low-mass planet migrates from inside the dead zone, it opens a gap, slowing its migration. A massive planet like Jupiter, in contrast, opens a gap and slows down inside the dead zone, independent of its initial orbital radius. The final orbital radius of a Jupiter-mass planet depends on the dead zone's viscosity. For the range of a-values noted above, this can vary from 7 AU to an orbital radius of 0.1 AU, which is characteristic of the hot Jupiters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".