A SEARCH FOR SUB-km KUIPER BELT OBJECTS WITH THE METHOD OF SERENDIPITOUS STELLAR OCCULTATIONS
Bibliographic record
Abstract
The results of a search for sub-km Kuiper Belt Objects (KBOs) with the method of serendipitous stellar occultations are reported. Photometric time series were obtained on the 1.8 m telescope at the Dominion Astrophysical Observatory (DAO) in Victoria, British Columbia, and were analyzed for the presence of occultation events. Observations were performed at 40 Hz and included a total of 5.0 star-hours for target stars in the ecliptic open cluster M35 (β = 0.9°), and 2.1 star-hours for control stars in the off-ecliptic open cluster M34 (β = 25.7°). To evaluate the recovery fraction of the analysis method, and thereby determine the limiting detectable size, artificial occultation events were added to simulated time series (1/ f scintillation-like power spectra), and to the real data. No viable candidate occultation events were detected. This limits the cumulative surface density of KBOs to 3.5 × 10 10 deg −2 (95% confidence) for KBOs brighter than m R = 35.3 (larger than ∼860 m in diameter, assuming a geometric albedo of 0.04 and a distance of 40 AU). An evaluation of trans-Neptunian object (TNO) occultations reported in the literature suggests that they are unlikely to be genuine, and an overall 95% confidence upper limit on the surface density of 2.8 × 10 9 deg −2 is obtained for KBOs brighter than m R = 35 (larger than ∼1 km in diameter, assuming a geometric albedo of 0.04 and a distance of 40 AU) when all existing surveys are combined.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".