An Extremely Deep Rubin Survey to Explore the Extended Kuiper Belt and Identify Objects Observable by New Horizons
Bibliographic record
Abstract
Abstract A proposed Vera C. Rubin Observatory deep-drilling microsurvey of the Kuiper Belt will investigate key properties of the distant solar system. Utilizing 30 hr of Rubin time across six 5 hr visits over 1 yr starting in summer 2026, the survey aims to discover and determine orbits for up to 730 Kuiper Belt objects (KBOs) to an r -magnitude of 27.5. These discoveries will enable precise characterization of the KBO size distribution, critical for understanding planetesimal formation. By aligning the survey field with NASA’s New Horizons spacecraft trajectory, the microsurvey will facilitate discoveries for the mission operating in the Kuiper Belt. Modeling based on the Outer Solar System Origin Survey predicts at least 12 distant KBOs observable with the New Horizons LOng Range Reconnaissance Imager (LORRI) and approximately three objects within 1 au of the spacecraft, allowing higher-resolution observations than Earth-based facilities. LORRI’s high-solar-phase-angle monitoring will reveal these objects’ surface properties and shapes, potentially identifying contact binaries and orbit-class surface correlations. The survey could identify a KBO suitable for a future spacecraft flyby. The survey’s size, depth, and cadence design will deliver transformative measurements of the Kuiper Belt’s size distribution and rotational properties across distance, size, and orbital class. The high stellar density in the survey field also offers synergies with transiting exoplanet studies.
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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 | 0.001 |
| 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 | 0.001 |
| 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".