Layup: orbit fitting at LSST Scale
Bibliographic record
Abstract
Starting later this year, the Vera C. Rubin’s Observatory’s Legacy Survey of Space and Time (LSST) will begin discovering millions of new Solar System objects, ranging from the closest near-Earth asteroids to the most distant trans-Neptunian objects. Fitting the orbits of those objects (the process of taking the observed on-sky positions and velocities of newly discovered moving Solar System objects and transforming them into orbital parameters) is essential to LSST Solar System science. To address this challenge, we present Layup, a modern and open-source orbit fitting software suite built in Python and C++, driven with the ephemeris quality orbit integrator ASSIST (Holman et al 2023) and built in collaboration with the LINCC Framework team of software engineers, as part of their Incubator program. In addition to handling the large data volume expected from LSST, Layup also has the capability to handle observations from shift-and-stack and ranging data, routines for converting orbits and uncertainties across different formats, and a special-purpose integrator to derive cometary semi-major axes upon entry in the planetary region, as well as accurate, publication quality visualization tools.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.015 |
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".