Col-OSSOS: The Two Types of Kuiper Belt Surfaces
Bibliographic record
Abstract
Abstract The Colors of the Outer Solar System Origins Survey (Col-OSSOS) has gathered a high-quality, near-simultaneous, and brightness-complete sample of (g − r) and (r − J) colors for 102 Kuiper Belt objects (KBOs) with (u − g) and (r − z) gathered for some. We present the current state of the survey and data analysis. Recognizing that the optical colors of most icy bodies broadly follow the reddening curve, we present a new projection of the optical−near-IR (NIR) colors, which rectifies the main nonlinear features in the optical−NIR along the ordinates. We find evidence for a bifurcation in the projected colors that presents itself as a diagonal empty region in the optical−NIR. A reanalysis of past color surveys reveals the same bifurcation. We interpret this as evidence for two separate surface classes: the BrightIR class spans the full range of optical colors and broadly follows the reddening curve, while the FaintIR objects are limited in optical color and are less bright in the NIR than the BrightIR objects. We present a two-class model. Objects in each class consist of a mix of separate blue and red materials and span a broad range in color. Spectra are modeled as linear optical and NIR spectra with different slopes that intersect at some transition wavelength. The underlying spectral properties of the two classes reproduce the main observed structures in the UV−optical−NIR color space (0.4 μm ≲ λ ≲ 1.4 μm), including the bifurcation observed in the Col-OSSOS and H/WTSOSS data sets, including the tendency for cold classical KBOs to have lower (r − z) colors than excited objects, and the well-known bimodal optical color distribution.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".