A Bayesian re-analysis of HD 11964: evidence for three planets
Bibliographic record
Abstract
Astronomers searching for the small signals induced by planets inevitably face significant statistical challenges. Bayesian inference has the potential of improving the interpretation of existing observations, the planning of future observations and ultimately inferences concerning the overall population of planets. This paper illustrates how a re‐analysis of published radial velocity data sets with a Bayesian multi‐planet Kepler periodogram is providing strong evidence for additional planetary candidates. The periodogram is implemented with a Markov chain Monte Carlo (MCMC) algorithm that employs an automated adaptive control system. For HD 11964, the data has been re‐analyzed using 1, 2, 3 and 4 planet models. The most probable model exhibits three periods of 38.02−0.22+0.11, 360−4+4, and 1924−43+44 d, and eccentricities of 0.22−0.22+0.11, 0.63−0.17+0.34, and 0.05−0.05+0.03, respectively Assuming the three signals (each one consistent with a Keplerian orbit) are caused by planets, the corresponding limits on planetary mass (M sin i) and semi‐major axis are (0.090−0.14+0.15MJ, 0.253−0.009+0.009au), (0.21−0.07+0.06MJ, 1.13−0.04+0.04au), (0.77−0.08+0.08MJ, 3.46−0.13+0.13au), respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".