The Kuiper Belt luminosity function from mR = 22 to 25
Bibliographic record
Abstract
In summer 1999, we performed a survey optimized for the discovery of irregular satellites of Uranus and Neptune. We imaged 11.85 deg2 of sky and discovered 66 new outer Solar system objects (not counting the three new Uranian satellites). Given the very short orbital arcs of our observations, only the heliocentric distance can be reliably determined. We were able to model the radial distribution of trans-Neptunian objects (TNOs). Our data support the idea of a strong depletion in the surface density beyond 45 au. After fully characterizing this survey's detection efficiency as a function of object magnitude and rate of motion, we find that the apparent luminosity function of the trans-Neptunian region in the range mR = 22–25 is steep with a best-fitting cumulative power-law index of α≃ 0.76 with one object per deg2 estimated at magnitude Ro = 23.3. This steep slope, corresponding to a differential size index of q≃ 4.8, agrees with other older and more recent analyses for the luminosity function brighter than 25 mag. A double power-law fit to the new data set turns out to be statistically unwarrented; this large and homogeneous data set provides no evidence for a break in the power-law slope, which must eventually occur if the Bernstein et al. sky density measurements are correct.
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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 | 0.000 |
| 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) | 0.002 | 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".