Shedding light on sub-Neptune formation from the evolution of their orbital period distributions
Bibliographic record
Abstract
Do sub-Neptunes assemble close to where we see them or do they form full-fledged farther away from their host star then migrate inwards?We address this question by studying the distribution of their orbital periods, one of the most fundamental observable parameters.Under migration, planet occurrence rate decreases towards shorter orbital periods, a feature that can be erased by subsequent orbital instabilities.Presently, the observed sub-Neptune period distribution is flat, down to 10 days, inside which we see a drop.We present our REBOUND N-body simulation results to demonstrate that these collisional mergers establish the observed flat orbital period distribution within tens of thousands of years, irrespective of their initial migration history.Our results suggest that much of the signature of migration is dynamically erased away shortly after the disk gas dissipates.i 4.7 Multiplicity distributions for systems in approach 2, compared to observations.For the observations, not all systems contain exclusively sub-Neptunes.Observed systems peak at 1 planet per system, while the simulated systems peak at 5 planets per system. . . . . . . . . . . . . . . . . . . .
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 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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".