OSSOS. VII. 800+ trans-Neptunian objects - the complete data release
Bibliographic record
Abstract
The Outer Solar System Origins Survey (OSSOS), a wide-field imaging program in 2013-2017 with the Canada-France-Hawaii Telescope, surveyed 155 deg$^{2}$ of sky to depths of $m_r = 24.1$-25.2. We present 838 outer Solar System discoveries that are entirely free of ephemeris bias. This increases the inventory of trans-Neptunian objects (TNOs) with accurately known orbits by nearly 50%. Each minor planet has 20-60 Gaia/Pan-STARRS-calibrated astrometric measurements made over 2-5 oppositions, which allows accurate classification of their orbits within the trans-Neptunian dynamical populations. The populations orbiting in mean-motion resonance with Neptune are key to understanding Neptune's early migration. Our 313 resonant TNOs, including 132 plutinos, triple the available characterized sample and include new occupancy of distant resonances out to semi-major axis $a \sim 130$ au. OSSOS doubles the known population of the non-resonant Kuiper belt, providing 436 TNOs in this region, all with exceptionally high-quality orbits of $a$ uncertainty $σ_{a} \leq 0.1\%$; they show the belt exists from $a \gtrsim 37$ au, with a lower perihelion bound of $35$ au. We confirm the presence of a concentrated low-inclination $a\simeq 44$ au "kernel" population and a dynamically cold population extending beyond the 2:1 resonance. We finely quantify the survey's observational biases. Our survey simulator provides a straightforward way to impose these biases on models of the trans-Neptunian orbit distributions, allowing statistical comparison to the discoveries. The OSSOS TNOs, unprecedented in their orbital precision for the size of the sample, are ideal for testing concepts of the history of giant planet migration in the Solar System.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.034 |
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".