Constraining atmospheric parameters and surface magnetic fields with <tt>ZeeTurbo</tt>: an application to SPIRou spectra
Bibliographic record
Abstract
ABSTRACT We report first results on a method aimed at simultaneously characterizing atmospheric parameters and magnetic properties of M dwarfs from high-resolution near-IR spectra recorded with SPIRou in the framework of the SPIRou Legacy Survey (SLS). Our analysis relies on fitting synthetic spectra computed from marcs model atmospheres to selected spectral lines, both sensitive and insensitive to magnetic fields. We introduce a new code, ZeeTurbo, obtained by including the Zeeman effect and polarized radiative transfer capabilities to Turbospectrum. We compute a grid of synthetic spectra with ZeeTurbo for different magnetic field strengths and develop a process to simultaneously constrain Teff, log g, $\rm {[M/H]}$, $\rm {[\alpha /Fe]}$, and the average surface magnetic flux. In this paper, we present our approach and assess its performance using simulations, before applying it to six targets observed in the context of the SLS, namely AU Mic, EV Lac, AD Leo, CN Leo, PM J18482+0741, and DS Leo. Our method allows us to retrieve atmospheric parameters in good agreement with the literature, and simultaneously yields surface magnetic fluxes in the range 2–4 kG with a typical precision of 0.05 kG, in agreement with literature estimates, and consistent with the saturated dynamo regime in which most of these stars are.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".