Modelling the RV jitter of early-M dwarfs using tomographic imaging
Bibliographic record
Abstract
In this paper, we show how tomographic imaging (Zeeman–Doppler imaging, ZDI) can be used to characterize stellar activity and magnetic field topologies, ultimately allowing us to filter out the radial velocity (RV) activity jitter of M dwarf moderate rotators. This work is based on spectropolarimetric observations of a sample of five weakly active early-M dwarfs (GJ 205, GJ 358, GJ 410, GJ 479, GJ 846) with HARPS-Pol and NARVAL. These stars have <it>v</it> sin <it>i</it> and RV jitters in the range 1–2 km s−1 and 2.7–10.0 m s−1 rms, respectively. Using a modified version of ZDI applied to sets of phase-resolved least-squares deconvolved profiles of unpolarized spectral lines, we are able to characterize the distribution of active regions at the stellar surfaces. We find that dark spots cover less than 2 per cent of the total surface of the stars of our sample. Our technique is efficient at modelling the rotationally modulated component of the activity jitter, and succeeds at decreasing the amplitude of this component by typical factors of 2–3 and up to 6 in optimal cases. From the rotationally modulated time series of circularly polarized spectra and with ZDI, we also reconstruct the large-scale magnetic field topology. These fields suggest that bistability of dynamo processes observed in active M dwarfs may also be at work for moderately active M dwarfs. Comparing spot distributions with field topologies suggest that dark spots causing activity jitter concentrate at the magnetic pole and/or equator, to be confirmed with future data on a larger sample.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".