MEASURING THE ABUNDANCE OF SUB-KILOMETER-SIZED KUIPER BELT OBJECTS USING STELLAR OCCULTATIONS
Bibliographic record
Abstract
We present here the analysis of about 19,500 new star hours of low ecliptic latitude observations (| b | ⩽ 20°) obtained by the Hubble Space Telescope 's Fine Guidance Sensors over a time span of more than nine years, which is in addition to the ∼12, 000 star hours previously analyzed by Schlichting et al. Our search for stellar occultations by small Kuiper Belt Objects (KBOs) yielded one new candidate event corresponding to a body with a 530 ± 70 m radius at a distance of about 40 AU. Using bootstrap simulations, we estimate a probability of ≈5% that this event is due to random statistical fluctuations within the new data set. Combining this new event with the single KBO occultation reported by Schlichting et al. we arrive at the following results: (1) the ecliptic latitudes of 6 6 and 14 4 of the two events are consistent with the observed inclination distribution of larger, 100-km-sized KBOs. (2) Assuming that small, sub-kilometer-sized KBOs have the same ecliptic latitude distribution as their larger counterparts, we find an ecliptic surface density of KBOs with radii larger than 250 m of N ( r > 250 m) = 1.1 +1.5 − 0.7 × 10 7 deg −2 ; if sub-kilometer-sized KBOs have instead a uniform ecliptic latitude distribution for −20° < b < 20° then N ( r > 250 m) = 4.4 +5.8 − 2.8 × 10 6 deg −2 . This is the best measurement of the surface density of sub-kilometer-sized KBOs to date. (3) Assuming the KBO size distribution can be well described by a single power law given by N (> r )∝ r 1 − q , where N (> r ) is the number of KBOs with radii greater than r , and q is the power-law index, we find q = 3.8 ± 0.2 and q = 3.6 ± 0.2 for a KBO ecliptic latitude distribution that follows the observed distribution for larger, 100-km-sized KBOs and a uniform KBO ecliptic latitude distribution for −20° < b < 20°, respectively. (4) Regardless of the exact power law, our results suggest that small KBOs are numerous enough to satisfy the required supply rate for the Jupiter family comets. (5) We can rule out a single power law below the break with q > 4.0 at 2σ, confirming a strong deficit of sub-kilometer-sized KBOs compared to a population extrapolated from objects with r > 45 km. This suggests that small KBOs are undergoing collisional erosion and that the Kuiper Belt is a true analog to the dust producing debris disks observed around other stars.
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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 | 0.000 | 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".