Mitigating stellar activity jitter with different line lists for least-squares deconvolution
Bibliographic record
Abstract
Context. Stellar activity limits the radial velocity (RV) search and characterisation of exoplanets, as it introduces spurious noise (called jitter) in the data sets and prevents the correct retrieval of a planetary signal. This is key for M dwarfs, considering that they manifest high activity levels and are primary targets for present and future searches of habitable Earth-like planets. To perform precise RV measurements, multi-line numerical techniques like cross-correlation and least-squares deconvolution (LSD) are typically employed. Aims. Effective filtering of activity is crucial to achieving the sensitivity required for small planet detections. Here we analyse the impact of selecting different line lists for LSD on the dispersion in our RV data sets, to identify the line list that most effectively reduces the jitter. Methods. We employ optical spectropolarimetric observations of the active M dwarf EV Lac collected with ESPaDOnS and NARVAL, and study two line down-selection approaches: a parametric method based on line properties (depth, wavelength, magnetic sensitivity) and a randomised algorithm that samples the line combination space. We test the latter further to find the line list that singles out the activity signal from other sources of noise, and on AD Leo and DS Leo to examine its consistency at mitigating jitter for different activity levels. The analysis is complemented with planetary injection tests. Results. The parametric selection yields a RV RMS reduction of less than 10%, while the randomised selection yields a systematic improvement (>50%) regardless of the activity level of the star examined. Furthermore, if activity is the dominant source of noise, this approach allows the construction of lists containing mainly activity-sensitive lines, which could be used to enhance the rotational modulation of the resulting data sets and determine the stellar rotation period more robustly. Finally, the output line lists allow the recovery of a synthetic planet (0.3–0.6 MJup on a 10 d orbit) in the presence of both moderate (20 m s−1 semi-amplitude) and high (200 m s−1) activity levels, without substantially affecting the planet signal (between 60 and 120 m s−1).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".