An apodized Kepler periodogram for separating planetary and stellar activity signals
Bibliographic record
Abstract
A new apodized Keplerian (AK) model is proposed for the analysis of precision radial velocity (RV) data to model both planetary and stellar activity (SA) induced RV signals. A symmetrical Gaussian apodization function with unknown width and centre can distinguish planetary signals from SA signals on the basis of the span of the apodization window. The general model for m AK signals includes a linear regression term between RV and the SA diagnostic log (R′hk), as well as an extra Gaussian noise term with unknown standard deviation. The model parameters are explored using a Bayesian fusion Markov chain Monte Carlo code. A differential version of the generalized Lomb–Scargle periodogram that employs a control diagnostic provides an additional way of distinguishing SA signals and helps guide the choice of new periods. Results are reported for a recent international RV blind challenge which included multiple state-of-the-art simulated data sets supported by a variety of SA diagnostics. In the current implementation, the AK method achieved a reduction in SA noise by a factor of approximately 6. Final parameter estimates for the planetary candidates are derived from fits that include AK signals to model the SA components and simple Keplerians to model the planetary candidates. Preliminary results are also reported for AK models augmented by a moving average component that allows for correlations in the residuals.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".