The spectral impact of magnetic activity on disc-integrated HARPS-N solar observations: exploring new activity indicators
Bibliographic record
Abstract
ABSTRACT Stellar activity is the major roadblock on the path to finding true Earth-analogue planets with the Doppler technique. Thus, identifying new indicators that better trace magnetic activity (i.e. faculae and spots) is crucial to aid in disentangling these signals from that of a planet’s Doppler wobble. In this work, we investigate activity related features as seen in disc-integrated spectra from the HARPS-N solar telescope. We divide high-activity spectral echelle orders by low-activity master templates (as defined using both $\log {R^{\prime }_{HK}}$ and images from the Solar Dynamics Observatory, SDO), creating ‘relative spectra’. With resolved images of the surface of the Sun (via SDO), the faculae and spot filling factors can be calculated, giving a measure of activity independent of, and in addition to, $\log {R^{\prime }_{HK}}$. We find pseudo-emission (and pseudo-absorption) features in the relative spectra that are similar to those reported in our previous work on α Cen B. In α Cen B, the features are shown to correlate better to changes in faculae filling factor than spot filling factor. In this work, we more confidently identify changes in faculae coverage of the visible hemisphere of the Sun as the source of features produced in the relative spectra. Finally, we produce trailed spectra to observe the radial velocity component of the features, which show that the features move in a redward direction as one would expect when tracking active regions rotating on the surface of a star.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".