The Trojan-like Colors of Low-perihelion Kuiper Belt Objects
Bibliographic record
Abstract
Abstract An important testable prediction of dynamical instability models for the early evolution of the solar system is that Jupiter Trojans share a source population with the Kuiper Belt. Concrete evidence of this prediction remains elusive, as Kuiper Belt objects (KBOs) and Jupiter Trojans appear to have different surface compositions. We address the long-standing question of Trojan origin by finding a dynamical subpopulation in the Kuiper Belt with Trojan-like colors. Combining existing photometric data with our own surveys on Keck I and Palomar P200, we find that the low-perihelion (q < 30 au, a > 30 au) component of the Kuiper Belt has colors that bifurcate similarly to the Jupiter Trojans, unlike Centaurs (a < 30 au), which have redder, Kuiper Belt-like colors. To connect the Jupiter Trojans to the Kuiper Belt, we test whether the distinct Trojan-like colors of low-perihelion KBOs result from surface processing or are sourced from a specific population in the Kuiper Belt. By simulating the evolution of the Canada–France Ecliptic Plane Survey synthetic population of KBOs for four billion years, we find that differences in heating timescales cannot result in a significant depletion of very red low-perihelion KBOs as compared to the Centaurs. We find that the neutrally colored scattered disk objects (e > 0.6 KBOs) contribute more to the low-perihelion KBO population than to Centaurs, resulting in their different colors.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".