Wapiti: A data-driven approach to correct for systematics in RV data
Bibliographic record
Abstract
Context. Recent advances in the development of precise radial velocity (RV) instruments in the near-infrared (near-IR) domain, such as SPIRou, have facilitated the study of M-type stars to more effectively characterize planetary systems. However, the near-IR presents unique challenges in exoplanet detection due to various sources of planet-independent signals which can result in systematic errors in the RV data. Aims. In order to address the challenges posed by the detection of exoplanetary systems around M-type stars using near-IR observations, we introduced a new data-driven approach for correcting systematic errors in RV data. The effectiveness of this method is demonstrated through its application to the star GJ 251. Methods. Our proposed method, Weighted principAl comPonent reconsTructIon (referred to as Wapiti), used a dataset of per-line RV time series generated by the line-by-line (LBL) algorithm and employed a weighted Principal Component Analysis (wPCA) to reconstruct the original RV time series. A multistep process was employed to determine the appropriate number of components, with the ultimate goal of subtracting the wPCA reconstruction of the per-line RV time series from the original data in order to correct systematic errors. Results. The application of Wapiti to GJ 251 successfully eliminated spurious signals from the RV time series and enabled the first detection in the near-IR of GJ 251b, a known temperate super-Earth with an orbital period of 14.2 days. This demonstrates that, even when systematics in SPIRou data are unidentified, it is still possible to effectively address them and fully realize the instrument’s capability for exoplanet detection. Additionally, in contrast to the use of optical RVs, this detection did not require us to filter stellar activity, highlighting a key advantage of near-IR RV measurements.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".