A search for Kuiper belt occultations using the Weizmann Fast Astronomical Survey Telescope
Bibliographic record
Abstract
ABSTRACT Measuring the size distribution of small (kilometre-scale) Kuiper belt objects (KBOs) can help constrain models of Solar system formation and planetary migration. Such small, distant bodies are hard to detect with current or planned telescopes, but can be identified as sub-second occultations of background stars. We present the analysis of data from the Weizmann Fast Astronomical Survey Telescope, consisting of fast photometry of ∼106 star-hours at a frame rate of 10–25 Hz. Our pipeline utilizes a matched-filter approach with a large template bank, including red-noise treatment, and injection of simulated events for estimating the detection efficiency. The KBO radius at which our survey is 10 per cent (50 per cent) efficient is 1.1 (2.0) km. The data from 2020–2021 observing seasons were analysed and no occultations were identified. We discuss a sample of sub-second false-positive events, both occultation-like and flare-like, which are still not fully understood but could be instructive for future surveys looking for short-duration events. We use our null-detection result to set limits on the kilometre-scale KBO number density. Our individual radius bin limits are consistent with most previous works, with N(r > 1 km) ⪅ 106 deg−2 (95 per cent confidence limit). Our integrated (all size) limits, assuming a power law normalized to large (≈45 km) KBOs give a power-law index q < 3.93 (95 per cent confidence limit). Finally, our results are in tension with a recently reported KBO detection from the ground, at the p = 4 × 10−4 level.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".