THE ABSOLUTE MAGNITUDE DISTRIBUTION OF KUIPER BELT OBJECTS
Bibliographic record
Abstract
Here we measure the absolute magnitude distributions ( H -distribution) of the dynamically excited and quiescent (hot and cold) Kuiper Belt objects (KBOs), and test if they share the same H -distribution as the Jupiter Trojans. From a compilation of all useable ecliptic surveys, we find that the KBO H -distributions are well described by broken power laws. The cold population has a bright-end slope, , and break magnitude, ( r '-band). The hot population has a shallower bright-end slope of, , and break magnitude . Both populations share similar faint-end slopes of α 2 ∼ 0.2. We estimate the masses of the hot and cold populations are ∼0.01 and ∼3 × 10 −4 M ⊕ . The broken power-law fit to the Trojan H -distribution has α 1 = 1.0 ± 0.2, α 2 = 0.36 ± 0.01, and H B = 8.3. The Kolmogorov–Smirnov test reveals that the probability that the Trojans and cold KBOs share the same parent H -distribution is less than 1 in 1000. When the bimodal albedo distribution of the hot objects is accounted for, there is no evidence that the H -distributions of the Trojans and hot KBOs differ. Our findings are in agreement with the predictions of the Nice model in terms of both mass and H -distribution of the hot and Trojan populations. Wide-field survey data suggest that the brightest few hot objects, with , do not fall on the steep power-law slope of fainter hot objects. Under the standard hierarchical model of planetesimal formation, it is difficult to account for the similar break diameters of the hot and cold populations given the low mass of the cold belt.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".