Improving MCMC Convergence for Joint Astrometry and Radial Velocity Orbit-fits Through Reparameterization
Bibliographic record
Abstract
Abstract The exoplanet orbit-fitting software package orbitize ! was initially designed to fit the orbits of directly imaged planets with relative astrometric measurements using a Markov Chain Monte Carlo (MCMC) algorithm. Since the publication of orbitize! v1.0, the ability to jointly fit radial velocities and astrometry has been incorporated. We first implemented a Basis class into orbitize! that enables users to add and fit in various orbit parameterizations. We then introduced a radial velocity-focused parameterization of the Keplerian orbital elements for the joint radial velocity and astrometry fits. We compared MCMC convergence speeds of the new radial velocity-focused basis to the original orbitize! standard basis for the system HD 190771, which has full orbital coverage in radial velocity data. We found a 16% faster convergence in time with the radial velocity-focused basis. We encourage users to consider using this basis when doing joint radial velocity and astrometry fits.
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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 | 0.011 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".