Chromaticity of stellar activity in radial velocities
Bibliographic record
Abstract
Context. In the search for exoplanets using radial velocities (RV), stellar activity has become one of the main limiting factors for detectability. Fortunately, activity-induced RV signals are wavelength-dependent or chromatic, unlike planetary signals. This study exploits the broad spectral coverage provided by the combined use of SOPHIE and SPIRou velocimeters to investigate the chromatic nature of the activity signal of the highly active M dwarf EV Lac. Aims. We aim to understand the origin of the strong wavelength dependence (chromaticity) observed in the RV signal of EV Lac by selecting spectral lines based on physical properties. In particular, we explore the impact of starspots by defining the contrast effect at the level of individual lines. The Zeeman effect is also considered in this study. Methods. SPIRou and SOPHIE spectra were reduced using the line-by-line (LBL) method. We performed custom RV calculations, using groups of spectral lines selected for their sensitivity to either the spot-to-photosphere contrast or the Zeeman effect. The sensitivity of each line to the spot is defined using a two-temperature model based on PHOENIX spectra, while Landé factors were used to quantify Zeeman sensitivity. Results. We find that the spectral lines are distributed in two distinct families of contrasts, producing anti-correlated RV signals. This leads to a partial cancellation of the total RV signal, especially at longer wavelengths and provides a natural explanation for the strong chromaticity observed in EV Lac. This sign-reversal effect is demonstrated here, for the first time, on empirical data. Building on this discovery, we propose a new approach to constraining spot temperatures and to mitigating stellar activity. This will open up promising avenues for improving activity corrections and enhancing the detection of exoplanets around active M dwarfs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".