ASTROMETRIC MONITORING OF THE HR 8799 PLANETS: ORBIT CONSTRAINTS FROM SELF-CONSISTENT MEASUREMENTS
Bibliographic record
Abstract
ABSTRACT We present new astrometric measurements from our ongoing monitoring campaign of the HR 8799 directly imaged planetary system. These new data points were obtained with NIRC2 on the W.M. Keck II 10 m telescope between 2009 and 2014. In addition, we present updated astrometry from previously published observations in 2007 and 2008. All data were reduced using the SOSIE algorithm, which accounts for systematic biases present in previously published observations. This allows us to construct a self-consistent data set derived entirely from NIRC2 data alone. From this data set, we detect acceleration for two of the planets (HR 8799b and e) at >3 σ . We also assess possible orbital parameters for each of the four planets independently. We find no statistically significant difference in the allowed inclinations of the planets. Fitting the astrometry while forcing coplanarity also returns χ 2 consistent to within 1 σ of the best fit values, suggesting that if inclination offsets of ≲20° are present, they are not detectable with current data. Our orbital fits also favor low eccentricities, consistent with predictions from dynamical modeling. We also find period distributions consistent to within 1 σ with a 1:2:4:8 resonance between all planets. This analysis demonstrates the importance of minimizing astrometric systematics when fitting for solutions to highly undersampled orbits.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".