The Mean Longitudinal Magnetic Field and its Uses in Radial-Velocity Surveys
Bibliographic record
Abstract
This work focuses on the analysis of the mean longitudinal magnetic field as a stellar activity tracer in the context of small exoplanet detection and characterisation in radial-velocity (RV) surveys. We use SDO/HMI filtergrams to derive Sun-as-a-star magnetic field measurements, and show that the mean longitudinal magnetic field is an excellent rotational period detector and a useful tracer of the solar magnetic cycle. To put these results into context, we compare the mean longitudinal magnetic field to three common activity proxies derived from HARPS-N Sun-as-a-star data: the full-width at half-maximum, the bisector span and the S-index. The mean longitudinal magnetic field does not correlate with the RVs and therefore cannot be used as a one-to-one proxy. However, with high cadence and a long baseline, the mean longitudinal magnetic field outperforms all other considered proxies as a solar rotational period detector, and can be used to inform our understanding of the physical processes happening on the surface of the Sun. We also test the mean longitudinal magnetic field as a "stellar proxy" on a reduced solar dataset to simulate stellar-like observational sampling. With a Gaussian Process regression analysis, we confirm that the solar mean longitudinal magnetic field is the most effective of the considered indicators, and is the most efficient rotational period indicator over different levels of stellar activity. This work highlights the need for polarimetric time series observations of stars.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".