THE RESONANT TRANS-NEPTUNIAN POPULATIONS
Bibliographic record
Abstract
The trans-Neptunian objects (TNOs) trapped in mean-motion resonances with Neptune were likely emplaced there during planet migration late in the giant-planet formation process. We perform detailed modeling of the resonant objects detected in the Canada–France Ecliptic Plane Survey (CFEPS) in order to provide population estimates and, for some resonances, constrain the complex internal orbital element distribution. Detection biases play a critical role because phase relationships with Neptune make object discovery more likely at certain longitudes. This paper discusses the 3:2, 5:2, 2:1, 3:1, 5:1, 4:3, 5:3, 7:3, 5:4, and 7:4 mean-motion resonances, all of which had CFEPS detections, along with our upper limit on 1:1 Neptune Trojans (which is consistent with their small population estimated elsewhere). For the plutinos (TNOs in the 3:2 resonance) we refine the orbital element distribution given by Kavelaars et al. in 2009 and show that steep H -magnitude distributions ( N ( H )∝10 α H , with α = 0.8–0.9) are favored in the range H g = 8–9, and confirm that this resonance does not share the inclination distribution of the classical Kuiper Belt. We give the first population estimate for the 5:2 resonance and find that, to within the uncertainties, the population is equal to that of the 3:2 (≃13,000 TNOs with H g < 9.16), whereas the 2:1 population is smaller by a factor of 3–4 compared to the other two resonances. We also measure significant populations inhabiting the 4:3, 5:3, 7:3, 5:4, 7:4, 3:1, and 5:1 resonances, with H g < 9.16 ( D > 100 km) populations in the thousands. We compare our intrinsic population and orbital element distributions with several published models of resonant-TNO production; the most striking discrepancy is that resonances beyond the 2:1 are in reality more heavily populated than in published models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".