orbitize!: A Comprehensive Orbit-fitting Software Package for the High-contrast Imaging Community
Bibliographic record
Abstract
Abstract orbitize! is an open-source, object-oriented software package for fitting the orbits of directly imaged objects. It packages the Orbits for the Impatient (OFTI) algorithm and a parallel-tempered Markov Chain Monte Carlo (MCMC) algorithm into a consistent and intuitive Python API. orbitize! makes it easy to run standard astrometric orbit fits; in less than 10 lines of code, users can read in data, perform one fit using OFTI and another using MCMC, and make two publication-ready figures. Extensive pedagogical tutorials, intended to be navigable by both orbit-fitting novices and seasoned experts, are available on our documentation page. We have designed the orbitize! API to be flexible and easy to use/modify for unique cases. orbitize! was designed by members of the exoplanet imaging community to be a central repository for algorithms, techniques, and know-how developed by this community. We intend for it to continue to expand and change as the field progresses and new techniques are developed, and call for community involvement in this process. Complete and up-to-date documentation is available at orbitize.info , and the source code is available at github.com/sblunt/orbitize .
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.154 | 0.080 |
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".