A Bayesian periodogram finds evidence for three planets in HD 11964
Bibliographic record
Abstract
A Bayesian multiplanet Kepler periodogram has been developed for the analysis of precision radial velocity data. The periodogram employs a parallel tempering Markov chain Monte Carlo algorithm. The HD 11964 data have been re‐analysed using 1, 2, 3 and 4 planet models. Assuming that all the models are equally probable a priori, the three planet model is found to be ≥600 times more probable than the next most probable model which is a two planet model. The most probable model exhibits three periods of 38.02+0.06−0.05, 360+4−4 and 1924+44−43 d, and eccentricities of 0.22+0.11−0.22, 0.63+0.34−0.17 and 0.05+0.03−0.05, 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 semimajor axis are respectively. The small difference (1.3σ) between the 360‐d period and one year suggests that it might be worth investigating the barycentric correction for the HD 11964 data.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".