Col-OSSOS: Compositional Homogeneity of Three Kuiper Belt Binaries
Bibliographic record
Abstract
Abstract The surface characterization of Trans-Neptunian binaries (TNBs) is key to understanding the properties of the disk of planetesimals from which these objects formed. In the optical wavelengths, it has been demonstrated that most equal-sized component systems share similar colors, suggesting they have a similar composition. The color homogeneity of binary pairs contrasts with the overall diversity of colors in the Kuiper Belt, which was interpreted as evidence that Trans-Neptunian objects (TNOs) formed from a locally homogeneous and globally heterogeneous protoplanetary disk. In this paradigm, binary pairs must have formed early, before the dynamically hot TNOs were scattered out from their formation location. The latter inferences, however, relied on the assumption that the matching colors of the binary components imply matching composition. Here, we test this assumption by examining the component-resolved photometry of three TNBs found in the Outer Solar System Origins Survey: 505447 (2013 SQ99), 511551 (2014 UD225), and 506121 (2016 BP81), across the visible and J-band near-infrared wavelength range. We report similar colors within 2σ for the binary pairs, which is suggestive of similar reflectance spectra and hence surface composition. This advocates for gravitational collapse of pebble clouds as a possible TNO formation route. However, we stress that several similarly small TNOs, including at least one binary, have been shown to exhibit substantial spectral variability in the near-infrared, implying color equality of binary pairs is likely to be violated in some cases.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".