Detection of a divot in the size distribution of the Kuiper Belt's scattering objects
Bibliographic record
Abstract
We present a joint analysis of the Canada France Ecliptic Plane Survey's scattering objects, and the Kaib et al. (2011b) orbital model, measuring the scattering objects' size distribution. Scattering objects are Trans-Neptunian objects which are strongly interacting with Neptune, having scattering encounters which change their semimajor axes on short dynamical timescales. We reject a single power-law distribution at the 99% level, and find that a dearth of small objects is required. We present a novel parameterisation of a divot size distribution, which rises as a single power-law to a precipitous drop, then recovers as another single power-law of potentially different slope. We constrain the form of such a divot distribution, and find that divots are preferred over "knee" size distributions, which are found elsewhere in the literature for different populations. We present our preferred divot scenario, which rises as a single power-law of logarithmic slope α = 0.8 as absolute Hg magnitudes increase to sizes corresponding to D ~ 100 km, then dropping by a factor of about 6 in differential number, followed by another single power-law of logarithmic slope α = 0.5. Our interpretation is that this feature arose from the size distribution made by planetesimal formation and is now "frozen in" to the "hot" populations of the outer Solar System. From this we estimate there are 2‧10⁹ scattering objects with Hg < 18, allowing for enough to supply the nearby Jupiter Family comets. This interpretation nicely ties the "hot" populations together while simultaneously explaining the source of the Jupiter Family comets and the observed paucity of intermediate-sized (50-100 km) Neptune Trojans.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".