High-precision Orbit Fitting and Uncertainty Analysis of (486958) 2014 MU69
Bibliographic record
Abstract
Abstract NASA’s New Horizons spacecraft will conduct a close flyby of the cold-classical Kuiper Belt Object (KBO) designated (486958) 2014 MU69 on 2019 January 1. At a heliocentric distance of 44 au, “MU69” will be the most distant object ever visited by a spacecraft. To enable this flyby, we have developed an extremely high-precision orbit fitting and uncertainty processing pipeline, making maximal use of the Hubble Space Telescope’s Wide Field Camera 3 (WFC3) and pre-release versions of the ESA Gaia Data Release 2 (DR2) catalog. This pipeline also enabled successful predictions of a stellar occultation by MU69 in 2017 July. We describe how we process the WFC3 images to match the Gaia DR2 catalog, extract positional uncertainties for this extremely faint target (typically 140 photons per WFC3 exposure), and translate those uncertainties into probability distribution functions for MU69 at any given time. We also describe how we use these uncertainties to guide New Horizons, plan stellar occultions of MU69, and derive MU69's orbital evolution and long-term stability.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".