Revisiting radial velocity measurements of the K2-18 system with the line-by-line framework
Bibliographic record
Abstract
ABSTRACT The cross-correlation function and template matching techniques have dominated the world of precision radial velocities for many years. Recently, a new technique, named line-by-line, has been developed as an outlier resistant way to efficiently extract radial velocity content from high resolution spectra. We apply this new method to archival HARPS and CARMENES data sets of the K2-18 system. After reprocessing the HARPS data set with the line-by-line framework, we are able to replicate the findings of previous studies. Furthermore, by splitting the full wavelength range into sub-domains, we were able to identify a systematic chromatic correlation of the radial velocities in the reprocessed CARMENES data set. After post-processing the radial velocities to remove this correlation, as well as rejecting some outlier nights, we robustly uncover the signal of both K2-18 b and K2-18 c, with masses that agree with those found from our analysis of the HARPS data set. We then combine both the HARPS and CARMENES velocities to refine the parameters of both planets, notably resulting in a revised mass and period for K2-18 c of $6.99^{+0.96}_{-0.99}$ $\rm M_{\oplus }$ and 9.2072 ± 0.0065 d, respectively. Our work thoroughly demonstrates the power of the line-by-line technique for the extraction of precision radial velocity information.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".