c © ESO 2013 Astronomy &Astrophysics
Bibliographic record
Abstract
The inventory of the populations of trans-Neptunian objects (TNO) has grown considerably over the last decade. As for other groups of small bodies in our solar system, TNOs are expected to have experienced a collisional evolution owing to their mutual impacts. The knowledge of the statistics of collisions, including determination of the rate of mutual collisions and the distribution of the impact velocity, is indeed a fundamental prerequisite for developing models of collisional evolution. We revised the evaluation of those statistical parameters for TNOs provided more than ten years ago on the basis of a much more limited sample of objects than currently available. We used the Canada-France Ecliptic Plane Survey (CFEPS) L7 model to extract an unbiased sample of orbits for TNOs, while the statistical parameters of impact are computed using a statistical tool. We investigated the statistics of impacts among TNOs for the whole population and for different dynamical subgroups. Moreover, we investigated the statistics of collisions between subgroups with crossing orbits. The peculiar dynamical behavior of objects in resonant orbits is taken into account. Our present computation of the probabilities of collision are 20 % to 50 % lower than previous estimates, while mean impact velocities turn out to be about 70 % higher. For instance, the rate of collisions among Plutinos, expressed in terms of the so-called mean intrinsic probability of collision, results to be (3.90 ± 0.01) × 10−22 km−2 yr−1 and the mean impact velocity is 2.46 ± 0.01 km s−1. We also find that the distributions of impact velocities seem to be quite different from pure Maxwellian distributions. These results can be useful
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.124 | 0.096 |
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".